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% \title{An Encoder-Centric Framework for Multilingual Dimensional Aspect-Based Sentiment Analysis}
\title{ssurface3 at SemEval-2026 Task 3: Efficient Methods for Multilingual Dimensional Aspect-Based Sentiment Analysis}

% % --- AUTHORS ---
% \author{
%   Anatolii Frolov \\
%   Skolkovo Institute of Science and Technology \\ Moscow, Skolkovo\\
%   \texttt{ssurface3@gmail.com} \\
%   \And
%   Elisei Rykov \\
%   Skolkovo Institute of Science and Technology \\ Moscow, Skolkovo \\
%   \texttt{second.author@example.edu} \\
% }


% \author{
% \bf Anatolii Frolov\textsuperscript{1}\quad
% Elisei Rykov\textsuperscript{1}\\
% \textsuperscript{1}Skoltech\\
% \href{Anatolii.Frolov@skoltech.ru}{Anatolii.Frolov@skoltech.ru}
% }

\author{
\textbf{Anatolii Frolov}\textsuperscript{1}\quad
Elisei Rykov\textsuperscript{1}\\
\textsuperscript{1}Skoltech\\
% \texttt{\{name,surname\}@skoltech.ru}
\href{mailto:Anatolii.Frolov@skoltech.ru}{Anatolii.Frolov@skoltech.ru}
}

\begin{document}
\maketitle

\begin{abstract}
% This work focuses on addressing the challenge of Dimensional Aspect-Based Sentiment Analysis (dimABSA subtask 1). ~\cite{yu-etal-2026-semeval} The task requires predicting continuous values for Valence (emotion) and Arousal (intensity) for specific target aspects within a sentence. We tackle the problem of V\#A regression using state-of-the-art encoder and Large Language Model (LLM) architectures. To enhance model stability, we implemented a Bounded Regression framework via sigmoid normalization and a composite loss function to directly optimize leaderboard metrics. Furthermore, we employed intermediate pre-training on external affective datasets (EmoBank) ~\cite{buechel-hahn-2017-emobank} to achieve robust generalization across English, Chinese, and Cyrillic domains. Our results demonstrate that while multilingual encoders provide a strong baseline, LLM-based backbones with parameter-efficient fine-tuning offer a significant performance leap.
This paper describes our submission to the Dimensional Aspect-Based Sentiment Analysis (dimABSA)  Shared Task (Subtask 1), which requires predicting continuous Valence and Arousal scores for target aspects in multilingual reviews. We evaluate three approaches: prompting-based baselines, a multilingual encoder model, and a decoder-only LLM with supervised fine-tuning. Our main focus is efficient adaptation under multilingual data scarcity. We show that compact encoder and decoder models, when properly fine-tuned, achieve strong performance across languages and domains. To improve training stability and enforce valid predictions, we use a bounded regression formulation that maps outputs to the target score range. We also explore parameter-efficient fine-tuning and intermediate training on external affective data. Results show that prompting-based baselines are substantially weaker than supervised models. The multilingual encoder provides a strong and efficient baseline, while the best performance is achieved by a compact decoder model with parameter-efficient fine-tuning. Overall, our findings highlight the importance of careful fine-tuning and training design for multilingual dimensional sentiment analysis.The code is available in the GitHub repository\footnote{https://github.com/ssurface3/dimabsa}
\end{abstract}

\section{Introduction}
Traditionally, Aspect-Based Sentiment Analysis (ABSA) ~\cite{lee2026dimabsabuildingmultilingualmultidomain} has been formulated as a discrete classification task, where opinions are categorized as positive, negative, or neutral. However, this coarse-grained formulation fails to capture the complexity and intensity of human emotions. For example, a review stating \textbf{The soup was okay} and one stating \textbf{The soup was absolutely life-changing} may both be classified as ``Positive,'' despite conveying substantially different emotional intensities.

To address this limitation, recent work has shifted toward Dimensional Aspect-Based Sentiment Analysis (dimABSA). Instead of assigning a single discrete label, dimABSA maps sentiment to a continuous affective space defined by Valence (degree of pleasure) and Arousal (degree of excitement or intensity). As a result, the task becomes a fine-grained regression problem, requiring models to predict precise real-valued scores on a scale from 1.00 to 9.00.

In this work, we address the \texttt{dimABSA} challenge~\cite{yu-etal-2026-semeval} with a robust framework that combines multilingual encoders and LLMs. First, we stabilize training with a bounded regression architecture. Second, we improve the model's affective reasoning via intermediate pre-training on an external affective dataset EmoBank~\cite{buechel-hahn-2017-emobank}.  The combined pipeline helps mitigate the ``curse of multilinguality'' and yields strong performance across English, Chinese, and Cyrillic-script languages.

\section{Related Work}
The dimABSA task bridges the gap between discrete linguistic labels and the continuous nature of human affect. By utilizing the Valence-Arousal space, we can model nuanced emotional states that traditional categorical systems miss. This approach is particularly vital for cross-lingual applications, where the same sentiment label may carry different cultural or linguistic intensities.
% \subsection{Related Work}
The development of this problem began with SemEval-2014 Task 4~\cite{pontiki-etal-2014-semeval}, which highlighted the ambiguity of aspect terms and annotator disagreement. These tasks originally focused on classification (Positive, Negative, Conflict, Neutral). The continuous nature of ABSA was later emphasized based on the circumplex model of affect~\cite{russell1980circumplex}. Recently, SIGHAN-2024 shared tasks~\cite{xu-etal-2024-hitsz} reached high scores on Chinese datasets; however, Chinese is not a low-resource language. The dimABSA 2025 shared task expands this to Japanese, Russian, Tatar, and Ukrainian. Our work aims to address the performance gap in continuous ABSA for these diverse language families.

\section{System Overview}

Our approach to the dimABSA task consists of three complementary methods: a prompting-based baseline, an encoder-based model, and a decoder-based model. Together, these methods allow us to compare lightweight inference-time prompting with supervised encoder and decoder architectures in a multilingual setting.

% The encoder-based method is built on a multilingual mmBERT-style architecture and serves as a stable, computationally efficient approach for fine-grained regression. It is designed to produce precise valence and arousal predictions while remaining relatively compact.

% The decoder-based method is implemented with a Qwen-style auto-regressive architecture. Compared to the encoder-based model, it leverages generative modeling and a larger parameter space to better capture complex affective cues and cross-lingual patterns.

% By evaluating all three methods, we analyze the trade-offs between simplicity, efficiency, and modeling capacity, and examine how each paradigm behaves under multilingual data scarcity and domain-specific sentiment variation.
% Our approach to the Dimensional Aspect-Based Sentiment Analysis (dimABSA) task is built upon two distinct paradigms of the Transformer architecture: bidirectional encoders and auto-regressive decoders. By evaluating both mmBERT-based and Qwen-based frameworks, we aim to examine the trade-offs between traditional discriminative modeling and the cross-lingual reasoning capabilities of modern Large Language Models (LLMs).
% The encoder-based approach (Section 3.1) serves as a stable, computationally efficient baseline designed for precise token-level regression. In contrast, the decoder-based approach (Section 3.2) leverages a significantly larger parameter space  to capture complex affective nuances in diverse linguistic domains. This dual-track methodology allows us to identify the "performance ceiling" of each architecture when faced with the challenges of multilingual data scarcity and domain-specific sentiment shifts

\subsection{Prompting Baseline}
The prompting-based baseline serves as a simple and transparent reference point with little to no task-specific training. It allows us to assess how far instruction-following behavior alone can go on the dimABSA task. \par
% As shown in Figure ~\ref{fig:json_example}, the model receives an input sentence and a target aspect, from which it is prompted to predict continuous Valence and Arousal (VA) scores.s on the 1.0--9.0 scale. We evaluate the baseline in two prompting settings.
As shown in Figure~\ref{fig:json_example}, the model receives an input sentence and a target aspect (see Figure~\ref{fig:json_example}), from which it is prompted to predict continuous Valence and Arousal (VA) scores on the 1.0--9.0 scale.


In the zero-shot setting, the model receives only a task description, the input sentence, and the target aspect, without any labeled examples. This setting tests the model's internal alignment with the VA scale based solely on its instruction-following capabilities.

In the few-shot (K-shot) setting, we additionally provide \(k\) high-quality demonstrations, i.e., \([\text{Sentence}, \text{Aspect}, \text{Gold VA score}]\) triples, selected from the training set. These demonstrations help calibrate the model's numerical output range and adapt it to the target domain (e.g., restaurant or laptop reviews) before predicting the VA scores for the target aspect.

Zero-Shot Prompt is shown in the Figure~\ref{fig:zsprompt}. It is a basic prompt for generation, containing all the needed information. We provided four diverse examples covering different sentiment polarities and intensities to calibrate the model's numerical range Figure~\ref{fig:fsprompt}.


\subsection{Encoder-Based Baselines}
Our primary encoder-based model is built on the mmBERT architecture~\cite{marone2025mmbert}. We choose this checkpoint because it combines a strong pre-training recipe with broad multilingual coverage, including support for low-resource languages.

Specifically, we use the \texttt{mmBERT-base} configuration, which consists of 12 Transformer layers, a hidden size of 768, and 12 attention heads. The encoder is fine-tuned for dimABSA with a task-specific regression head.

To adapt the pre-trained encoder to the dimensional ABSA task, we attach a linear regression head to the final hidden state of the special classification token (\texttt{[CLS]}). Let $h_{\texttt{[CLS]}} \in \mathbb{R}^{768}$ denote the encoder representation of the \texttt{[CLS]} token. The prediction is computed as
\[
\hat{y} = W h_{\texttt{[CLS]}} + b,
\]
where $\hat{y} \in \mathbb{R}^{2}$ and $\hat{y} = [\hat{V}, \hat{A}]$ corresponds to the predicted Valence and Arousal scores.
\\ 
To ensure a comprehensive and meaningful comparison against industry-standard bidirectional encoders, we also benchmark our approach against several heavily vetted multilingual models. These include:
\begin{itemize}
\item \textbf{XLM-RoBERTa (Base and Large):} We include both configurations of XLM-R ~\cite{conneau2020unsupervisedcrosslingualrepresentationlearning} to evaluate the impact of model scale on dimensional ABSA and to provide a benchmark against the current industry workhorse for cross-lingual transfer.
\item \textbf{mDeBERTa-v3-base:} We evaluate mDeBERTa-v3 \cite{he2021debertav3}, which utilizes disentangled attention and ELECTRA-style pre-training. This represents the current state-of-the-art for transformer encoders in multilingual NLU tasks.
\end{itemize}
For a fair comparison, all encoder baselines utilize the same regression head architecture and input template described above. This allows us to isolate the architectural advantages of the decoder-based LoRA approach from the supervised encoding paradigm.

The input is formatted as a single sequence containing the review text and the target aspect term. To preserve aspect-centricity, we use a standard BERT-style input template:
\texttt{[CLS] Text [SEP] Aspect [SEP]} (and <s> for XLM-R).
% \subsection{Encoder-Based Method}
% Our primary encoder baseline is built upon Multilingual BERT (mmBERT) architecture ~\cite{marone2025mmbertmodernmultilingualencoder}. This specific checkpoint was chosen due to multiple state-of-the-art practices using while pre-training this model and because it is made to understand low-resource languages , something that other models fail to do.

% More precisely, we utilize the mmBERT-base configuration, which consists of 12 Transformer layers with a hidden dimension of 768 and 12 attention heads. The model is treated as a feature extractor that generates contextualized embeddings for the input sequence.

% To adapt the pre-trained encoder for the Dimensional ABSA task, we append a task-specific linear regression head to the final hidden state of the special classification token (\texttt{[CLS]}). The output of the encoder $h_{cls} \in R^{768}$is mapped to a two-dimensional vector $\hat{y} \in R^{2}$ using a fully connected layer: $\hat{y} = Wh_{cls} + b$ where $\hat{y} = [\hat{V}, \hat{A}]$  represents the predicted Valence and Arousal scores.
% Input Representation:
% The input to the model is formatted as a single sequence containing the review text and the target aspect term. To maintain aspect-centricity, we utilize the standard BERT-style input: \texttt{[CLS] + Text + [SEP] + Aspect + [SEP]}.


\subsection{Decoder-based Method}
Beyond the encoder-based baseline, we also employ a decoder-only Large Language Model (LLM) to leverage its cross-lingual reasoning capabilities and broad pre-trained knowledge of affective expressions. We use Qwen-2.5-1.5B as the backbone model~\cite{qwen2024qwen25} due to its strong performance-to-size ratio, which enables efficient training on consumer-grade hardware. 

To align the causal decoder with the regression objective, we use a descriptive prompt template that frames the task as an instruction-following problem. The input sequence is constructed as follows: \texttt{``Predict the valence and arousal scores (range 1-9) for the aspect              \{aspect\}' in the following sentence: `\{text\}' Valence and Arousal:'}.

By embedding the target \texttt{\{aspect\}} and the context \texttt{\{text\}} into this instructional scaffolding, we provide the model with explicit task semantics and the expected numerical range. This configuration allows the self-attention mechanism to perform cross-interaction between the aspect term and the sentimental cues present in the sentence. We extract the hidden state $\mathbf{h}_{last}$ from the final colon token in the sequence, using it as the contextual representation for the regression head.
\paragraph{Linear Head Fine-Tuning}
In this configuration, the 1.5B-parameter backbone is kept frozen, and only the final linear regression head is trained. Specifically, we update the weights of a linear layer that maps the 1536-dimensional hidden representation to a 2-dimensional output.

\paragraph{Low-Rank Adaptation (LoRA)}
We employ LoRA~\cite{hu2022lora} by injecting trainable low-rank matrices into the attention and MLP layers. This enables the model to adapt to multi-domain sentiment nuances while avoiding the memory overhead of full-parameter updates.

\paragraph{Full Fine-Tuning}
In the most computationally intensive configuration, we unfreeze all 1.5 billion parameters. This allows full adaptation to the dimABSA data distribution, but it also increases the risk of catastrophic forgetting, where the model's general-purpose multilingual knowledge may be overwritten by patterns specific to the training subsets.

% Beyond the encoder baseline, we employ a decoder-only Large Language Model (LLM) to leverage its superior cross-lingual reasoning and pre-trained knowledge of affective nuances. We select the Qwen-2.5-1.5B model~\cite{qwen2025qwen25technicalreport} as our backbone due to its competitive performance-to-size ratio, which allows for efficient training on consumer-grade hardware (NVIDIA RTX 2080 Ti).

% \paragraph{Regression-head-only} In this configuration, the entire 1.5B parameter backbone is frozen. Only the weights of the final 
% 1536 $->$ 2 linear layers are updated. 
% \paragraph{Low-Rank Adaptation (LoRA)} We employ LoRA~\cite{hu2021loralowrankadaptationlarge} to inject trainable low-rank matrices into the attention and MLP layers. This allows the model to adapt its internal attention mechanisms to the nuances of multi-domain sentiment without the memory overhead of full parameter updates. 
% \paragraph{Full Fine-Tuning} In the most intensive configuration, we unfreeze all 1.5 billion parameters. This allows for total model adaptation to the dimABSA distribution but increases the risk of catastrophic forgetting, where the model's general-purpose multilingual knowledge is overwritten by the specific patterns of the training subsets.


\section{Experimental Setup}

\subsection{Data}
The official dimABSA-2025 dataset covers six languages; however, several subsets suffer from severe data scarcity, especially the Cyrillic-script languages (Russian, Tatar, and Ukrainian). Detailed split statistics are provided in Table~\ref{tab:dataset_stats} in the Appendix.

% To alleviate data scarcity, we augment training with external affective datasets. Specifically, we combine EmoBank, Facebook Posts, and SIGHAN TrainSet1, and normalize all valence--arousal annotations to a common 9-point scale (since some datasets use a 1--5 scale). A summary of the external corpora and their original annotation scales is provided in Table~\ref{tab:external_data} in the Appendix. This augmentation approximately doubles the amount of training data.

\subsection{Hyperparameter Ablation}
We conduct a hyperparameter search over mmBERT fine-tuning settings to identify an effective configuration. The search space is summarized in Table~\ref{tab:params}, and selected results are reported in Table~\ref{tab:hyperparameter_tuning}.

To reduce the risk of catastrophic forgetting (i.e., degradation of the model's pre-trained multilingual capabilities), we perform checkpoint-wise evaluation throughout training. Specifically, we run inference after each epoch and select the checkpoint that provides the best fit on the evaluation split before performance begins to deteriorate, particularly on low-resource languages. This strategy improves robustness across both higher-resource domains and the more challenging Cyrillic-script and Tatar subsets.

For the decoder-based setup, we additionally study LoRA hyperparameters. The parameters used are shown in Table~\ref{tab:lora}.

To ensure that the predictions remain within the valid annotation range (1.0--9.0), we use a bounded regression formulation instead of hard clipping. Let $z_V$ and $z_A$ denote the unconstrained outputs for Valence and Arousal. We transform them as follows:
\[
\hat{V} = 1.00 + (9.00 - 1.00)\,\sigma(z_V),
\]
\[
\hat{A} = 1.00 + (9.00 - 1.00)\,\sigma(z_A),
\]
where $\sigma(\cdot)$ is the sigmoid function. This preserves the ordering and relative differences of predictions while enforcing the target range. For both methods, we primarily use Mean Squared Error (MSE) as the training objective.% We also experiment with specialized alternatives, including a custom RMSE-VA loss and a composite loss that incorporates Pearson correlation. However, in our preliminary experiments, standard MSE provides the most stable optimization behavior for cross-lingual regression.

% We conducted an ablation study on the hyperparameters of mmBERT to identify the optimal configuration for fine-tuning. The experimental setup is detailed in Table~\ref{tab:params}.

% Moreover, we had to clip some values due to the fact that sometimes models generated output more than 9 or less than 1. In order to preserve the effect of values over threshold , we hat to apply sigmoid function: 
% $\hat{V} = 1.00 + (9.00 - 1.00)* \sigma{(z_{v})}$
% $\hat{A} = 1.00 + (9.00 - 1.00)* \sigma{(z_{A})}$
% We primarily utilize the Mean Squared Error (MSE) as our objective function for both architectures. While we also experimented with specialized metrics—including a custom RMSE-VA loss and a Composite Loss (integrating Pearson Correlation)—our initial probes indicated that standard MSE provided the most stable gradient landscape for cross-lingual regression. \\

\begin{table*}[t]
  \centering
  \resizebox{\textwidth}{!}{
    \begin{tabular}{@{}llccccccccccc@{}}
\toprule
\textbf{Method}           & \textbf{Setup}             & \multicolumn{2}{c}{\textbf{English}} & \multicolumn{2}{c}{\textbf{Japanese}} & \textbf{Russian} & \textbf{Tatar} & \textbf{Ukrainian} & \multicolumn{3}{c}{\textbf{Chinese}}          & \textbf{Avg.} \\ \cmidrule(lr){3-12}
                          &                            & Laptop            & Rest.            & Finance           & Hotel             & Rest.            & Rest.          & Rest.              & Finance       & Laptop        & Rest.         &               \\ \midrule
\multirow{4}{*}{Baseline} & Zero-Shot (Qwen2.5-1.5B)   & 2.14              & 1.81             & 2.83              & 1.93              & 2.20             & 2.59           & 2.37               & 2.63          & 2.39          & 2.18          & 2.31          \\
                          & Few-Shot (Qwen2.5-1.5B)    & 1.40              & 1.39             & 2.14              & 1.74              & 1.75             & 2.43           & 1.88               & 2.12          & 1.91          & 1.82          & 1.86          \\
                          & Few-Shot (Kimi-k2)         & 2.19              & 2.15             & 1.64              & 1.76              & 1.78             & 1.94           & 1.78               & 1.97          & 1.64          & 1.90          & 1.87          \\
                          & QLoRA (Qwen3-14B)          & 2.81              & 2.64             & 1.90              & 2.29              & 2.15             & 2.64           & 2.21               & 1.47          & 1.77          & 2.01          & 2.19          \\ \midrule
\multirow{4}{*}{Encoder} 
& Full FT (mmBERT-base)        & 1.31 & 1.34 & 1.12 & 0.75 & 1.60 & 1.89 & 1.63 & 0.67 & 0.89 & 1.05 & 1.22 \\
& Full FT (XLM-R-base)         & 1.49 & 1.38 & 1.17 & 0.90 & 1.78 & 2.22 & 1.83 & 0.67 & 1.01 & 1.14 & 1.36 \\
& Full FT (XLM-R-large)        & 1.36 & 1.28 & 1.13 & 0.75 & 1.78 & 2.14 & 1.81 & 0.62 & 0.86 & 1.00 & 1.27 \\
& Full FT (mDeBERTa-v3-base)   & 2.13 & 2.08 & 1.42 & 1.33 & 2.17 & 2.22 & 2.17 & 0.87 & 1.28 & 1.36 & 1.70 \\
\midrule
\multirow{3}{*}{Decoder}  & Linear Head (Qwen2.5-1.5B) & \textbf{1.04}     & \textbf{0.91}    & 0.96              & 0.65              & \textbf{1.08}             & 1.63           & 1.29               & 0.75          & 0.81          & 0.91          & 1.00          \\
                          & Full FT (Qwen2.5-1.5B)     & \textbf{1.04}     & 0.94             & 1.27              & 0.66              & 1.34             & 1.70           & 1.50               & 1.02          & 0.88          & 0.85 & 1.12          \\
                          & LoRA (Qwen2.5-1.5B)        & 1.06 & 0.99 & 1.31 & 0.65 & 1.30 & 1.73 & 1.51 & 1.09 & 0.88 & \textbf{0.84} & 1.14 \\ 
\midrule
\multirow{2}{*}{Leaderboard} 
& Top-1 unofficial results  & 1.24 & 1.10 & \textbf{0.65} & \textbf{0.55} & 1.21 & \textbf{1.53} & \textbf{1.18} & \textbf{0.48} & \textbf{0.61} & 0.92 & 0.95 \\
&  Official Best (Team Name)      & LogSigma    & LogSigma    & TeleAI    & TeleAI    & PAI    & PAI    & PAI    & HUS@NLP-VNU    & TeleAI    & ICT-NLP    \\
\bottomrule
\end{tabular}
  }
  \caption{Comprehensive comparison across all experimental methods, reported in $RMSE_{VA}$ (lower is better).single-run estimates selected on the development set. Best result per subset in \textbf{bold}.}
  \label{tab:master_results_v2}
  
\end{table*}


\section{Results}

Overall results are reported in Table~\ref{tab:master_results_v2}. We observe a clear performance hierarchy across the evaluated paradigms: prompting-based baselines perform worst, the encoder-based model provides a strong supervised baseline, and decoder-based fine-tuning achieves the best overall results.

Among prompting baselines, few-shot prompting substantially improves over zero-shot prompting for Qwen2.5-1.5B (average $RMSE_{VA}$ drops from 2.31 to 1.86), confirming the importance of in-context calibration for the 1.0--9.0 VA scale. However, even the strongest prompting baselines (Qwen2.5-1.5B few-shot: 1.86; Kimi-k2 few-shot: 1.87) remain far behind supervised training. Notably, the larger Qwen3-14B QLoRA baseline still underperforms the smaller few-shot prompting baseline (2.19 vs. 1.86), highlighting that model scale alone is insufficient without task-specific adaptation. Table~\ref{tab:icl_results} summarizes the performance of the proposed prompting strategies.

The encoder-based models provide a robust supervised baseline. As shown in Table ~\ref{tab:results_encoders_all}, XLM-RoBERTa-Large achieves the strongest performance among bidirectional encoders with an average RMSE of 1.27. Interestingly, the mmBERT-base model (1.22) remains highly competitive despite its smaller parameter count, validating its specialized pre-training for multilingual tasks. In contrast, mDeBERTa-v3 exhibited higher error rates (1.70), which we attribute to the inherent instability of its disentangled attention mechanism when applied to continuous regression in high-variance multilingual settings. Crucially, all supervised encoders substantially outperform prompting-based baselines, confirming that instruction-following alone is insufficient for precise dimensional sentiment mapping.


Compared with the \textit{Top-1 System} (included as a leaderboard reference), our best model (LoRA on Qwen2.5-1.5B) achieves a comparable overall average (1.14 vs.\ 0.95), remaining competitive while using only 1.18\% of the backbone parameters. At the per-subset level, our LoRA model outperforms the reference on four subsets (both English domains, Russian, and Ukrainian), while the reference remains stronger on Japanese and Chinese subsets.



\begin{figure*}[t]
    \centering

    \begin{subfigure}[t]{0.49\textwidth}
        \centering
        \resizebox{\linewidth}{!}{
            \begin{tikzpicture}
                \begin{axis}[
                title={\textbf{Dev RMSE Over Time}},
                xlabel={Training Steps},
                ylabel={Dev RMSE},
                xmin=0, xmax=600,
                ymin=1.25, ymax=1.8,
                xtick={0,100,200,300,400,500,600},
                ytick={1.3,1.4,1.5,1.6,1.7,1.8},
                legend pos=outer north east,
                legend cell align={left},
                legend style={font=\tiny},
                grid=both,
                grid style={line width=.1pt, draw=gray!10},
                major grid style={line width=.2pt, draw=gray!20},
                % We set a fixed internal width, resizebox scales it to the page
                width=12cm, 
                height=8cm,
                mark size=1.5pt
            ]
            
            \addplot[dashed, gray, line width=1pt, domain=0:600] {1.3};
            % \addlegendentry{Performance Plateau (1.3)}

            % 1. dimabsa | lr=1e-5, epochs=4 (The Best Performing Model)
            \addplot[color=red, mark=*, line width=1pt] coordinates {
                (15,2.9185) (30,2.0058) (60,1.5460) (90,1.4834) (120,1.4346) (150,1.4432) 
                (180,1.4150) (210,1.4176) (240,1.4180) (270,1.3664) (300,1.3484) (330,1.3094) 
                (360,1.3208) (390,1.3155) (420,1.3192) (435,1.2965) (480,1.3194) (540,1.3071) (600,1.3097)
            };
            \addlegendentry{(1e-5, ep=4)}

            % 2. dimabsa | lr=1e-4, epochs=4 (High Learning Rate - Noisy)
            \addplot[color=olive, mark=square*, line width=0.8pt, dashed] coordinates {
                (15,1.9937) (45,1.6839) (75,1.5662) (120,1.3909) (150,1.6050) (195,1.3702) 
                (240,1.4264) (285,1.3342) (330,1.5069) (375,1.4000) (420,1.3113) (480,1.3793) 
                (540,1.3864) (600,1.3781)
            };
            \addlegendentry{(1e-4, ep=4)}

            % 3. dimabsa | lr=1e-5, epochs=3
            \addplot[color=blue, mark=triangle*, line width=0.7pt] coordinates {
                (15,2.2439) (45,1.6688) (90,1.5179) (135,1.4194) (180,1.4992) (225,1.4282) 
                (270,1.3499) (315,1.3645) (360,1.3543) (405,1.3394) (435,1.3140) (450,1.3181)
            };
            \addlegendentry{(1e-5, ep=3)}

            % 4. dimabsa | lr=1e-4, epochs=3
            \addplot[color=cyan, mark=diamond*, line width=0.7pt, dashed] coordinates {
                (15,1.9250) (60,1.4917) (105,1.3481) (150,1.4195) (195,1.4030) (240,1.3436) 
                (285,1.3202) (330,1.3781) (375,1.4013) (420,1.3151) (450,1.3953)
            };
            \addlegendentry{(1e-4, ep=3)}

            % 5. dimabsa | lr=1e-4, epochs=2
            \addplot[color=purple, mark=pentagon*, line width=0.6pt, dotted] coordinates {
                (15,1.8484) (45,1.4633) (90,1.5914) (135,1.3273) (180,1.3827) (225,1.4070) 
                (270,1.3525) (300,1.3314)
            };
            \addlegendentry{(1e-4, ep=2)}

            % 6. dimabsa | lr=1e-5, epochs=2
            \addplot[color=orange, mark=x, line width=0.6pt] coordinates {
                (15,2.3390) (60,1.7051) (105,1.5433) (150,1.4490) (195,1.4416) (240,1.4201) 
                (285,1.4080) (300,1.4202)
            };
            \addlegendentry{(1e-5, ep=2)}

            % 7. dimabsa | lr=1e-4, epochs=1
            \addplot[color=gray, mark=+, line width=0.5pt, dashdotted] coordinates {
                (15,1.8828) (45,1.4362) (75,1.6084) (105,1.4185) (135,1.3849) (150,1.3581)
            };
            \addlegendentry{(1e-4, ep=1)}

            % 8. dimabsa | lr=1e-5, epochs=1
            \addplot[color=magenta, mark=|, line width=0.5pt] coordinates {
                (15,2.4790) (60,1.5879) (90,1.5149) (120,1.5034) (150,1.5103)
            };
            \addlegendentry{(1e-5, ep=1)}

            % 9. dimabsa | lr=2e-5, epochs=1 (Short trial)
            \addplot[color=teal, mark=o, line width=0.8pt] coordinates {
                (15,2.2673) (30,1.8757)
            };
            \addlegendentry{(2e-5, ep=1)}
            \end{axis}
            \end{tikzpicture}
        }
        \caption{Encoder-based approach trained on the original dimABSA dataset only.}
        \label{fig:rmse_plot}
    \end{subfigure}
    \hfill
    \begin{subfigure}[t]{0.49\textwidth}
        \centering
        \resizebox{\linewidth}{!}{
            \begin{tikzpicture}
                \begin{axis}[
                title={\textbf{Dev RMSE Over time}},
                xlabel={Training Steps},
                ylabel={Dev RMSE on the English-Laptop},
                xmin=0, xmax=750,
                ymin=1.2, ymax=2.5, % Adjusted to show convergence clearly
                xtick={0,150,300,450,600,750},
                ytick={1.2,1.4,1.6,1.8,2.0,2.2,2.4},
                legend pos=outer north east,
                legend cell align={left},
                legend style={font=\tiny},
                grid=both,
                grid style={line width=.1pt, draw=gray!10},
                major grid style={line width=.2pt, draw=gray!20},
                width=12cm,
                height=8cm,
                mark size=1.5pt
            ]

            % 1. lr=1e-5, epochs=4 (Red)
            \addplot[color=red, mark=*, line width=1pt] coordinates {
                (45,1.9420) (90,1.7247) (135,1.7360) (180,1.6460) (225,1.5086) (270,1.5750) 
                (315,1.4482) (360,1.4455) (405,1.4495) (450,1.5212) (495,1.5275) (540,1.4703) 
                (585,1.4538) (630,1.4733) (690,1.4524) (750,1.4693)
            };
            \addlegendentry{1e-5, ep=4}

            % 2. lr=1e-4, epochs=4 (Olive - Dashed)
            \addplot[color=olive, mark=square*, line width=0.8pt, dashed] coordinates {
                (15,2.0787) (45,1.6634) (90,1.5209) (135,1.5814) (180,1.4752) (225,1.3999) 
                (270,1.3528) (285,1.3221) (330,1.4192) (375,1.4308) (420,1.5086) (480,1.4400) 
                (540,1.4031) (600,1.4615) (660,1.4256) (750,1.4127)
            };
            \addlegendentry{1e-4, ep=4}

            % 3. lr=1e-4, epochs=3 (Cyan - Dashdotted)
            \addplot[color=cyan, mark=triangle*, line width=0.7pt, dashdotted] coordinates {
                (15,2.0436) (45,1.4979) (90,1.5400) (135,1.3624) (180,1.4359) (225,1.3405) 
                (270,1.3321) (315,1.4428) (360,1.3861) (405,1.4509) (450,1.4519) (555,1.4270)
            };
            \addlegendentry{1e-4, ep=3}

            % 4. lr=1e-5, epochs=3 (Blue)
            \addplot[color=blue, mark=diamond*, line width=0.7pt] coordinates {
                (15,3.0801) (45,1.8355) (90,1.5426) (135,1.6287) (180,1.5900) (225,1.5036) 
                (270,1.4737) (315,1.4686) (360,1.4098) (405,1.4502) (495,1.4088) (555,1.4026)
            };
            \addlegendentry{1e-5, ep=3}

            % 5. lr=1e-5, epochs=2 (Purple)
            \addplot[color=purple, mark=pentagon*, line width=0.6pt] coordinates {
                (15,2.9048) (45,1.9069) (90,1.6160) (135,1.6582) (180,1.6058) (225,1.5815) 
                (270,1.5432) (315,1.5319) (375,1.5083)
            };
            \addlegendentry{1e-5, ep=2}

            % 6. lr=1e-4, epochs=2 (Orange - Dotted)
            \addplot[color=orange, mark=x, line width=0.6pt, dotted] coordinates {
                (15,2.2455) (45,1.9552) (90,1.5585) (135,1.4376) (180,1.5516) (225,1.3747) 
                (315,1.3511) (375,1.4070)
            };
            \addlegendentry{1e-4, ep=2}

            % 7. lr=1e-4, epochs=1 (Gray)
            \addplot[color=gray, mark=+, line width=0.5pt, dashed] coordinates {
                (15,2.1137) (45,1.5767) (75,1.5755) (105,1.3707) (135,1.4322) (180,1.4338)
            };
            \addlegendentry{1e-4, ep=1}

            % 8. lr=1e-5, epochs=1 (Magenta)
            \addplot[color=magenta, mark=|, line width=0.5pt] coordinates {
                (15,4.0932) (45,2.0499) (75,1.6639) (135,1.5958) (180,1.6369)
            };
            \addlegendentry{1e-5, ep=1}

            % Plateau Line
            % \addplot[dashed, black, line width=1pt, domain=0:750] {1.3};
            % \addlegendentry{Plateau (1.3)}

            % Best Checkpoint Star (Based on your best with_chinese value)
            \node[star, star point height=2pt, minimum size=8pt, draw=black, fill=yellow, label={above:\scriptsize Best (1.3221)}] 
                at (axis cs:285,1.3221) {};

            \end{axis}
            \end{tikzpicture}
        }
        \caption{Encoder-based approach trained on the augmented dataset.}
        \label{figure:chinese_cong}
    \end{subfigure}

    \caption{Convergence curves across hyperparameter configurations for the encoder-based approach under (left) original and (right) augmented training data.}
    \label{fig:encoder_convergence_side_by_side}
\end{figure*}
\subsection{Distributional Mismatch and Augmentation Failure}
As noted in our initial experiments, intermediate training on EmoBank and Facebook datasets improved development performance but led to a degradation on the hidden test set. To investigate this, we visualize the density distributions of the DimABSA training data compared to the EmoBank augmentation data
\begin{figure}[htbp]
    \centering
    \includegraphics[width=1.0\linewidth]{Distribution_Overlay.pdf}
    \caption{Kernel Density Estimation (KDE) plots of Valence (left) and Arousal (right) distributions. The EmoBank augmentation data shows a extreme ``neutrality bias'' (spike at 5.0), whereas the DimABSA target domain is significantly more polarized and bimodal.}
    \label{fig:dist_shift}
\end{figure}
The KDE plots
(graphs~\ref{fig:violin_dist}) reveal a severe Neutrality Bias in the augmentation data. EmoBank’s distribution is characterized by a sharp, narrow spike at the mean (5.0 on a 1–9 scale). In contrast, the DimABSA target domain is significantly more polarized and bimodal , particularly in the Valence dimension.
For Arousal, the mismatch is even more pronounced: while EmoBank treats most sentences as neutral intensity, DimABSA labels are widely distributed with a significant shift toward higher intensity values (6.0–8.0). By injecting EmoBank data, the model's prior was inadvertently "pulled" toward the neutral center, causing it to lose the sensitivity required to predict the extreme aspect-level sentiment values found in reviews.This explains the drop in test performance and justifies our final decision to use a LoRA-tuned model trained exclusively on in-domain data. The mismatch likely stems from domain differences: EmoBank contains sentence-level annotations of general text (news, blogs), whereas DimABSA targets aspect-level sentiment in product and hotel reviews — a substantially more polarized and opinion-dense register. Future work could address this via domain-adaptive filtering or re-weighting of augmentation samples to better match the target distribution.

\\
The distribution of Arousal in Figure ~\ref{fig:dist_shift} further explains the performance gap compared to Valence. While Valence shows clear bimodal peaks (showing distinct positive and negative clusters), Arousal in the DimABSA data forms a wide, flat plateau. This lack of clear density peaks suggests that Arousal is more linguistically diverse and lacks the high-frequency 'anchor' words that Valence possesses. The model struggles to find a correlation because the intensity of an aspect is often context-dependent rather than explicitly lexicalized
\subsection{Encoder Baseline and Data Augmentation Ablation}

As the number of epochs increases, most configurations show a clear trend toward convergence, suggesting that mmBERT needs a larger number of optimization steps to reliably align multilingual representations with the continuous Valence--Arousal (VA) space. Although the higher learning rate of \(1\times10^{-4}\) achieves lower RMSE on the development set in some runs, this improvement does not transfer to the held-out English-Laptop dev split used for final model selection. We therefore select \(1\times10^{-5}\) as the preferred learning rate due to its more stable generalization behavior.

Despite different early-stage trajectories, most configurations eventually converge to a narrow error band (approximately 1.30--1.40 RMSE) on the English-Laptop test set. In practice, this regime is typically reached within 400--600 optimization steps when using smaller learning rates.

To improve the encoder's sentiment modeling in English and Chinese, we augment training with additional data from EmoBank and Chinese-EmoBank. Fine-tuning on the augmented corpus yields mixed results:
\begin{enumerate}
    \item Performance on the development set improves, but test-set performance slightly degrades, suggesting a distribution mismatch.
    \item Training time increases substantially due to the larger corpus size.
    \item The development set does not always appear to be representative of the corresponding test-set distribution.
\end{enumerate}

As shown in the convergence curves for the augmented-data variant in Figure~\ref{figure:chinese_cong}, training exhibits substantially higher optimization noise than the baseline runs. The larger and more heterogeneous training corpus creates a more complex optimization landscape, which increases sensitivity to the learning rate. This behavior is especially pronounced for the \(1\times10^{-4}\) configurations (olive curve), which show frequent RMSE spikes. We interpret this as evidence that the model struggles to find a stable minimum that generalizes across the English and Chinese data distributions.
In addition, as shown in Table~\ref{tab:compact_stats}, augmentation with large English/Chinese resources further reduces the relative share of low-resource languages in the training mixture, which may weaken adaptation to Cyrillic-script and Tatar subsets.

% \subsection{Encoder-based Method}

% The ablation results on the original dimABSA dataset indicate that relatively small learning rates and a larger number of training epochs are needed for effective adaptation to the task.

% As shown in Table~\ref{tab:hyperparameter_tuning}, encoder performance is highly sensitive to the interaction between the learning rate (LR) and the number of training epochs. As the number of epochs increases, we observe a consistent trend toward convergence, suggesting that mmBERT requires more optimization steps to reliably map multilingual semantics to the continuous VA space. Interestingly, although the higher learning rate of \(1\times10^{-4}\) achieves a lower RMSE on the development set, this improvement does not transfer to the test set. We therefore select \(1\times10^{-5}\) as the preferred LR for more stable generalization.

% Despite different early training trajectories, most configurations eventually cluster within a narrow error range (approximately 1.30--1.40 RMSE) on the English-Laptop test set. This convergence toward a common error floor suggests that the mmBERT-base architecture is approaching its performance limit on this dataset, typically within 400--600 optimization steps when using a relatively small learning rate.


% To improve encoder's sentiment modeling in English and Chinese, we augment training with additional data from EmoBank and Chinese-EmoBank. Fine-tuning on the augmented corpus yields mixed results:
% \begin{enumerate}
%     \item The additional data improves performance on the development set but slightly degrades performance on the test set, suggesting a distribution mismatch.
%     \item Training time increases substantially due to the larger corpus size.
%     \item The development set does not always appear to be representative of the corresponding test-set distribution.
% \end{enumerate}

% As shown in the convergence curves for the augmented-data variant (Figure~\ref{figure:chinese_cong}), training exhibits substantially higher optimization noise than the baseline runs. The increased data volume and heterogeneity of sources create a more complex optimization landscape, which makes the model more sensitive to the learning rate. This behavior is particularly visible for the \(1\times10^{-4}\) configurations (olive curve), which show frequent RMSE spikes. We interpret this as evidence that the model struggles to find a stable minimum that generalizes across the  English and Chinese distributions. Moreover, it even more distills low-resourse languages as they get even less percentage of training dataset~\ref{tab:compact_stats}


% \subsection{Encoder Baseline and Ablation}
% The ablation results on the original dimABSA dataset (Table~\ref{tab:hyperparameter_tuning}) suggest that higher learning rates and a larger number of epochs are required to fully capture the semantics of the task.

% As illustrated in Table~\ref{tab:hyperparameter_tuning}, the encoder performance is highly sensitive to the interaction between the learning rate (LR) and the number of training epochs. As the amount of epochs increases, we observe a consistent trend toward convergence. This indicates that mmBERT requires a higher number of iterations to effectively map multilingual semantics to the continuous VA space. Interestingly, while the higher learning rate of 1e-4 achieved a lower RMSE on the development set, it did not translate to better test-set performance, leading us to select 1e-5 as the optimal LR for stable generalization

% Despite the varying starting trajectories, most configurations eventually cluster within a narrow error margin between 1.30 and 1.40 RMSE on an English-Laptop test. This convergence toward a similar 'error floor' suggests that the performance limit for the mmBERT-base architecture on this specific dataset is largely reached within 400–600 steps with relatively small learning rate 


% \subsection{Decoder-based method}
% As the model benefits from the examples , the next idea is to include more, so the model would better understand the semantics. 

% In this section we just tried to fine tune the final(Linear Layer) to test the backbone capabilities of the Qwen2.5-1.5B. We achieved it by freezing all the layers except for the last one and fine-tuning on the whole dataset for 5 epochs with LR 1e-5\\
% The results demonstrate that the Qwen-2.5-1.5B backbone possesses superior internal sentiment representations compared to the mmBERT-base encoder
% By freezing the Qwen-2.5-1.5B backbone and only training low-rank adapters we allowed the model to specialize in Valence-Arousal regression while preserving its extensive pre-trained multilingual knowledge. Only 1.18\% of the parameters were fine-tuned, preventing the problem of catastrophic forgetting that might be observed on full-fine-tune.

% Full fine-tuning resulted in the loss of generality but yielded good results. The performance trajectories illustrated in Figure~\ref{fig:all_trajectories} reveal the convergence dynamics of the Qwen-2.5-1.5B backbone under a full parameter fine-tuning regime. We observe a consistent global reduction in RMSE VA, with the average score stabilizing at approximately 1.12 by the sixth epoch. However, a significant dichotomy in learning velocity exists between linguistic domains: while high-resource tasks such as the Japanese Hotel and English Laptop datasets reach a performance plateau by epoch 4, the low-resource Tatar task exhibits a much slower descent. Despite the increased plasticity afforded by full parameter updates, the Tatar domain remains a persistent error bottleneck, confirming that the model requires more extensive iterations to align the affective semantics of Turkic languages compared to the more readily available representations of English or Japanese.

% We can see the better performance across all languages and metrics. The best jump in the performance can be seen on the dataset Chinese-Finance. Moreover, we can see that the model,finally, beat the mmBERT on the Tatar, simply by starting to understand the semantics of that language. 

\subsection{Decoder-Based Method}
Since the prompting baseline improves when provided with examples, we next evaluate supervised adaptation of a decoder-only backbone to better capture task-specific Valence--Arousal semantics.

We first consider a regression-head-only setting to probe the backbone capacity of Qwen2.5-1.5B. In this configuration, all backbone layers are frozen, and only the final linear layer is fine-tuned on the full training set for 5 epochs with a learning rate of \(1\times10^{-5}\). Even this minimal adaptation yields strong results, indicating that the Qwen2.5-1.5B backbone already contains useful multilingual affective representations.

We then apply LoRA to specialize the model for VA regression while preserving its pre-trained multilingual knowledge. In our setup, only 1.18\% of the parameters are updated. This parameter-efficient adaptation provides a favorable trade-off between specialization and stability, and helps reduce the risk of catastrophic forgetting compared to full fine-tuning.

Full fine-tuning also produces strong results, but it is less stable and tends to lose some of the model's generality. Figure~\ref{fig:all_trajectories} shows the convergence dynamics of Qwen2.5-1.5B under full parameter updates. The average \(RMSE_{VA}\) decreases consistently during training and eventually stabilizes around 1.12. At the same time, convergence speed differs across domains: higher-resource subsets such as English-Laptop and Japanese-Hotel reach a plateau earlier, whereas the low-resource Tatar subset improves more slowly and remains a persistent bottleneck.

Overall, decoder-based fine-tuning improves performance across languages relative to the encoder baseline. The largest gains are observed on Chinese-Finance, and the decoder-based models also outperform mmBERT on Tatar, suggesting better adaptation to underrepresented languages when the decoder backbone is fine-tuned appropriately.

\subsection{Statistical Significance of LoRA solution}
To address potential variance resulting from random initialization, we conducted an empirical stability test for our best-performing method (LoRA-tuned Qwen2.5-1.5B). As shown in Table ~\ref{tab:lora_seeds}, we report the performance across three independent random seeds ($s \in \{7, 42, 1337\}$). 
The results demonstrate remarkable stability, with an average overall RMSE of $1.14 \pm 0.01$. The standard deviation across all subsets remains extremely low (ranging from 0.01 to 0.06), confirming that the performance gains are an inherent property of the model architecture and the parameter-efficient fine-tuning strategy, rather than a result of favorable random initialization. This statistical consistency provides strong evidence for the reliability of our proposed method in multilingual settings.
% The final comparison:
% \begin{table}[H]
%   \centering
%   \resizebox{\columnwidth}{!}{
%     \begin{tabular}{llcc}
%     \toprule
%     \textbf{Language} & \textbf{Domain} & \textbf{Linear Head} & \textbf{LoRA Adaptation} \\
%     \midrule
%     English (ENG) & Laptop & \textbf{1.0427} & 1.0570 \\
%     English (ENG) & Restaurant & \textbf{0.9094} & 0.9291 \\
%     Japanese (JPN) & Finance & 0.9581 & \textbf{0.8215} \\
%     Japanese (JPN) & Hotel & 0.6549 & \textbf{0.6387} \\
%     Russian (RUS) & Restaurant & 1.0815 & \textbf{0.9562} \\
%     Tatar (TAT) & Restaurant & 1.6287 & \textbf{1.5018} \\
%     Ukrainian (UKR) & Restaurant & 1.2911 & \textbf{1.1590} \\
%     Chinese (ZHO) & Finance & 0.7535 & \textbf{0.6572} \\
%     Chinese (ZHO) & Laptop & 0.8062 & \textbf{0.7237} \\
%     Chinese (ZHO) & Restaurant & 0.9067 & \textbf{0.8900} \\
%     \midrule
%     \textbf{Average} & \textbf{All Tasks} & \textbf{1.0033} & \textbf{0.9334} \\
%     \bottomrule
%     \end{tabular}
%   }
% \caption{Comparative performance of Qwen-2.5-1.5B: Linear Probing (Head Only) vs. LoRA Adaptation. Metrics are reported in $RMSE_{VA}$ (lower is better).}
%       \label{tab:linear_vs_lora}
% \end{table}

% \subsection{Comparison with Competition Benchmarks}
% To contextualize the efficacy of our \texttt{Qwen2.5-1.5B} framework, we compare our performance against the top-ranked submissions from the dimABSA shared task.

% \begin{table}[H]
%   \centering
%   \resizebox{\columnwidth}{!}{
%     \begin{tabular}{llcc}
%     % \toprule
%     \textbf{Language} & \textbf{Domain} & \textbf{dimABSA-2025 Top} & \textbf{Qwen-LoRA} \\
%     \midrule
%     English (ENG) & Laptop & 1.2408 & \textbf{1.0570} \\
%     English (ENG) & Restaurant & 1.1035 & \textbf{0.9291} \\
%     Japanese (JPN) & Finance & \textbf{0.6581} & 0.8215 \\
%     Japanese (JPN) & Hotel & \textbf{0.5561} & 0.6387 \\
%     Russian (RUS) & Restaurant & 1.2190 & \textbf{0.9562} \\
%     Tatar (TAT) & Restaurant & 1.5294 & \textbf{1.5018} \\
%     Ukrainian (UKR) & Restaurant & 1.1888 & \textbf{1.1590} \\
%     Chinese (ZHO) & Finance & \textbf{0.4841} & 0.6572 \\
%     Chinese (ZHO) & Laptop & \textbf{0.6103} & 0.7237 \\
%     Chinese (ZHO) & Restaurant & 0.9256 & \textbf{0.8900} \\
%     \midrule
%     \textbf{Average} & \textbf{All Tasks} & 0.9516 & \textbf{0.9334} \\
%     \bottomrule
%     \end{tabular}
%   }
%   \caption{Comparison between our best-performing model (Qwen-LoRA) and the dimABSA top submission benchmarks. All values are reported in RMSE-VA (lower is better).}
%   \label{tab:competition_comparison}
% \end{table}
% \begin{table*}[t] 
%   \centering
%   \resizebox{\textwidth}{!}{
%     \begin{tabular}{lcccccc}
%     \toprule
%     \textbf{Task / Dataset} & \textbf{Zero-Shot} & \textbf{Few-Shot} & \textbf{mmBERT} & \textbf{Linear Head} & \textbf{Full FT} & \textbf{LoRA (Ours)} \\
%     & (Prompting) & (k=4) & (Encoder) & (Decoder) & (Decoder) & (Decoder) \\
%     \midrule
%     English Laptop & 2.1429 & 1.4008 & 1.3069 & 1.0427 & 1.1967 & \textbf{1.0570} \\
%     English Restaurant & 1.8112 & 1.3944 & 1.3369 & 0.9094 & 1.1603 & \textbf{0.9291} \\
%     Japanese Finance & 2.8303 & 2.1442 & 1.1182 & 0.9581 & 1.3294 & \textbf{0.8215} \\
%     Japanese Hotel & 1.9282 & 1.7401 & 0.7458 & 0.6549 & 0.7813 & \textbf{0.6387} \\
%     Russian Restaurant & 2.2030 & 1.7451 & 1.6021 & 1.0815 & 1.3409 & \textbf{0.9562} \\
%     Tatar Restaurant & 2.5857 & 2.4319 & 1.8936 & 1.6287 & 1.7527 & \textbf{1.5018} \\
%     Ukrainian Restaurant & 2.3704 & 1.8754 & 1.6304 & 1.2911 & 1.5225 & \textbf{1.1590} \\
%     Chinese Finance & 2.6308 & 2.1156 & 0.6650 & 0.7535 & 1.0582 & \textbf{0.6572} \\
%     Chinese Laptop & 2.3916 & 1.9058 & 0.8880 & 0.8062 & 0.9443 & \textbf{0.7237} \\
%     Chinese Restaurant & 2.1780 & 1.8196 & 1.0467 & 0.9067 & 0.8705 & \textbf{0.8900} \\
%     \midrule
%     \textbf{Average RMSE $\downarrow$} & 2.3072 & 1.8573 & 1.2233 & 1.0033 & 1.1957 & \textbf{0.9334} \\
%     Average $PCC_V \uparrow$ & 0.4415 & 0.6512 & 0.7926 & 0.7472 & 0.5916 & \textbf{0.8071} \\
%     Average $PCC_A \uparrow$ & 0.0268 & 0.2766 & 0.4868 & 0.4268 & 0.3074 & \textbf{0.5052} \\
%     \bottomrule
%     \end{tabular}
%   }
% \caption{Comprehensive performance comparison across all experimental configurations. Values in the main grid represent the joint $RMSE_{VA}$ (lower is better). The bottom rows summarize the average Pearson Correlation across all tasks.}
%   \label{tab:master_results}
% \end{table*}


% \begin{table}
%   \centering
%   \resizebox{\columnwidth}{!}{
%     \begin{tabular}{llccc}
%     \toprule
%     \textbf{Language} & \textbf{Domain} & \textbf{PCC\_V} & \textbf{PCC\_A} & \textbf{RMSE\_VA } \\
%     \midrule
%     English (ENG) & Laptop & 0.8044 & 0.5091 & 1.1967 \\
%     English (ENG) & Restaurant & 0.8500 & 0.5961 & 1.1603 \\
%     Japanese (JPN) & Finance & 0.3795 & 0.0367 & 1.3294 \\
%     Japanese (JPN) & Hotel & 0.8746 & 0.5789 & 0.7813 \\
%     Russian (RUS) & Restaurant & 0.7065 & 0.2405 & 1.3409 \\
%     Tatar (TAT) & Restaurant & 0.1118 & 0.0254 & 1.7527 \\
%     Ukrainian (UKR) & Restaurant & 0.5446 & 0.1836 & 1.5225 \\
%     Chinese (ZHO) & Finance & 0.3308 & 0.0955 & 1.0582 \\
%     Chinese (ZHO) & Laptop & 0.5930 & 0.2748 & 0.9443 \\
%     Chinese (ZHO) & Restaurant & 0.7205 & 0.5336 & 0.8705 \\
%     \midrule
%     \textbf{Average (Linear Head)} & \textbf{All Tasks} & \textbf{0.5916} & \textbf{0.3074} & \textbf{1.1957} \\
%     \bottomrule
%     \end{tabular}
%   }
%   \caption{Performance results for \textbf{Linear Head} (Qwen-2.5-1.5B)}\label{tab:full_ft_results}
% \end{table}
% \begin{table*}[t]
%   \centering
%   \resizebox{\textwidth}{!}{
%     \begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}
%     \toprule
%     \textbf{Task} & \multicolumn{3}{c|}{\textbf{Zero-Shot}} & \multicolumn{3}{c|}{\textbf{Few-Shot (k=4)}} & \multicolumn{3}{c|}{\textbf{mmBERT (MSE)}} & \multicolumn{3}{c|}{\textbf{Linear Head}} & \multicolumn{3}{c|}{\textbf{Full FT}} & \multicolumn{3}{c}{\textbf{LoRA (Ours)}} \\
%     & $P_V$ & $P_A$ & $R$ & $P_V$ & $P_A$ & $R$ & $P_V$ & $P_A$ & $R$ & $P_V$ & $P_A$ & $R$ & $P_V$ & $P_A$ & $R$ & $P_V$ & $P_A$ & $R$ \\
%     \midrule
%     Eng. Laptop & 0.47 & -0.09 & 2.14 & 0.74 & 0.36 & 1.40 & 0.85 & 0.54 & 1.31 & 0.86 & 0.50 & 1.04 & 0.80 & 0.51 & 1.20 & 0.86 & 0.55 & \textbf{1.06} \\
%     Eng. Rest.  & 0.56 & -0.03 & 1.81 & 0.78 & 0.35 & 1.39 & 0.84 & 0.59 & 1.34 & 0.89 & 0.64 & 0.91 & 0.85 & 0.60 & 1.16 & 0.90 & 0.66 & \textbf{0.93} \\
%     Jpn. Fin.   & 0.35 & 0.10 & 2.83 & 0.54 & 0.12 & 2.14 & 0.68 & 0.18 & 1.12 & 0.69 & 0.17 & 0.96 & 0.38 & 0.04 & 1.33 & 0.79 & 0.29 & \textbf{0.82} \\
%     Jpn. Hotel  & 0.62 & 0.02 & 1.93 & 0.85 & 0.36 & 1.74 & 0.90 & 0.69 & 0.75 & 0.92 & 0.64 & 0.65 & 0.87 & 0.58 & 0.78 & 0.94 & 0.73 & \textbf{0.64} \\
%     Rus. Rest.  & 0.54 & 0.11 & 2.20 & 0.78 & 0.22 & 1.75 & 0.83 & 0.50 & 1.60 & 0.85 & 0.51 & 1.08 & 0.71 & 0.24 & 1.34 & 0.88 & 0.59 & \textbf{0.96} \\
%     Tat. Rest.  & 0.10 & 0.06 & 2.59 & 0.16 & 0.12 & 2.43 & 0.68 & 0.41 & 1.89 & 0.23 & 0.15 & 1.63 & 0.11 & 0.03 & 1.75 & 0.47 & 0.28 & \textbf{1.50} \\
%     Ukr. Rest.  & 0.43 & 0.08 & 2.37 & 0.66 & 0.24 & 1.88 & 0.82 & 0.50 & 1.63 & 0.73 & 0.39 & 1.29 & 0.54 & 0.18 & 1.52 & 0.77 & 0.50 & \textbf{1.16} \\
%     Zho. Fin.   & 0.46 & 0.03 & 2.63 & 0.61 & 0.22 & 2.12 & 0.73 & 0.29 & 0.67 & 0.67 & 0.26 & 0.75 & 0.33 & 0.10 & 1.06 & 0.76 & 0.31 & \textbf{0.66} \\
%     Zho. Laptop & 0.39 & 0.06 & 2.39 & 0.69 & 0.33 & 1.91 & 0.78 & 0.61 & 0.89 & 0.80 & 0.53 & 0.81 & 0.59 & 0.27 & 0.94 & 0.84 & 0.63 & \textbf{0.72} \\
%     Zho. Rest.  & 0.49 & -0.07 & 2.18 & 0.71 & 0.45 & 1.82 & 0.80 & 0.57 & 1.05 & 0.83 & 0.47 & 0.91 & 0.72 & 0.53 & 0.87 & 0.85 & 0.50 & \textbf{0.89} \\
%     \midrule
%     \textbf{Average} & \textbf{0.44} & \textbf{0.03} & \textbf{2.31} & \textbf{0.65} & \textbf{0.28} & \textbf{1.86} & \textbf{0.79} & \textbf{0.49} & \textbf{1.22} & \textbf{0.75} & \textbf{0.43} & \textbf{1.00} & \textbf{0.59} & \textbf{0.31} & \textbf{1.20} & \textbf{0.81} & \textbf{0.51} & \textbf{0.93} \\
%     \bottomrule
%     \end{tabular}
%   }
%   \vspace{0.2cm}
%   \footnotesize{$P_V$: PCC Valence, $P_A$: PCC Arousal, $R$: RMSE VA.}
% \caption{Comprehensive performance comparison across all tasks and architectures. Metrics for each configuration are reported in columns ($PCC_V, PCC_A, RMSE_{VA}$).}
%   \label{tab:master_all_data}
% \end{table*}
% \begin{table}
%   \centering
%   \resizebox{\columnwidth}{!}{
%     \begin{tabular}{llccc}
%     \toprule
%     \textbf{Language} & \textbf{Domain} & \textbf{PCC\_V} & \textbf{PCC\_A} & \textbf{RMSE\_VA } \\
%     \midrule
%     English (ENG) & Laptop & 0.8639 & 0.5511 & 1.0570 \\
%     English (ENG) & Restaurant & 0.9026 & 0.6640 & 0.9291 \\
%     Japanese (JPN) & Finance & 0.7928 & 0.2903 & 0.8215 \\
%     Japanese (JPN) & Hotel & 0.9409 & 0.7338 & 0.6387 \\
%     Russian (RUS) & Restaurant & 0.8800 & 0.5910 & 0.9562 \\
%     Tatar (TAT) & Restaurant & 0.4677 & 0.2820 & 1.5018 \\
%     Ukrainian (UKR) & Restaurant & 0.7740 & 0.5009 & 1.1590 \\
%     Chinese (ZHO) & Finance & 0.7599 & 0.3131 & 0.6572 \\
%     Chinese (ZHO) & Laptop & 0.8426 & 0.6297 & 0.7237 \\
%     Chinese (ZHO) & Restaurant & 0.8468 & 0.4961 & 0.8900 \\
%     \midrule
%     \textbf{Average} & \textbf{All Tasks} & \textbf{0.8071} & \textbf{0.5052} & \textbf{0.9334} \\
%     \bottomrule
%     \end{tabular}
%   }
%   \caption{Performance results for the Qwen-2.5-1.5B model configuration across 10 multilingual tasks using LoRA adapters}\label{tab:lora_}
% \end{table}

% \section{Conclusion}

% \section{Error Analysis}
% To understand the model's failure modes, we performed a qualitative analysis of high-error predictions. A primary issue identified is the \textbf{Sentiment Flip} in contrastive sentences.

% \begin{table}[H]
%   \centering
%   \small
%   \renewcommand{\arraystretch}{1.3} % Adds vertical padding 
%   \begin{tabularx}{\columnwidth}{l X} % l = left-aligned, X = auto-wrap
%     \toprule
%     \textbf{Category} & \textbf{Details} \\
%     \midrule
%     \textbf{Sentence} & \textit{``Unfortunately, the waiter did sell the shrimp and grits with the additional crab as heaven in his mouth, but that it was not.''} \\
%     \textbf{Aspect}   & \textit{shrimp and grits} \\
%     \midrule
%     \textbf{Gold (V, A)} & (1.88, 5.70) \\
%     \textbf{Pred (V, A)} & (7.88, 5.93) \\
%     \midrule
%     \textbf{Observation} & \textbf{Sentiment Flip}: The model was distracted by the local positive keyword ``heaven'' and failed to process the global negation provided by ``Unfortunately'' and ``but that it was not.'' \\
%     \bottomrule
%   \end{tabularx}
% for a "prettier" look
%   \caption{Case Study: Qualitative analysis of a Valence prediction error.}
%   \label{tab:case_study}
% \end{table}
% The performance ceiling of the encoder-based architectures was happening exclusively due to this aspect of the sentences. There were some ideas to include the opinion or sentiment(positive/negative) but even LLMs (that hardware could inference in adequate time) couldn't produce adequate opinion or correct sentiment due to complexity of some languages. Some attempts to include either  opinion or sentiment didn't provide any positive results.\\
% Moreover, the distribution of development and test phases differed , therefore, evaluation on development yielded lesser results than evaluation on test. \\
% Finally, there was a problem of catastrophic forgetting in the full-fine-tunning process: 
% \begin{table}[H]
%   \centering
  
%   \resizebox{\columnwidth}{!}{
%     \begin{tabular}{llccc}
%     \toprule
%     \textbf{Language} & \textbf{Domain} & \textbf{PCC\_V $\uparrow$} & \textbf{PCC\_A $\uparrow$} & \textbf{RMSE\_VA $\downarrow$} \\
%     \midrule
%     English (ENG) & Laptop & 0.8044 & 0.5091 & 1.1967 \\
%     English (ENG) & Restaurant & 0.8500 & 0.5961 & 1.1603 \\
%     Japanese (JPN) & Finance & 0.3795 & 0.0367 & 1.3294 \\
%     Japanese (JPN) & Hotel & 0.8746 & 0.5789 & 0.7813 \\
%     Russian (RUS) & Restaurant & 0.7065 & 0.2405 & 1.3409 \\
%     Tatar (TAT) & Restaurant & 0.1118 & 0.0254 & 1.7527 \\
%     Ukrainian (UKR) & Restaurant & 0.5446 & 0.1836 & 1.5225 \\
%     Chinese (ZHO) & Finance & 0.3308 & 0.0955 & 1.0582 \\
%     Chinese (ZHO) & Laptop & 0.5930 & 0.2748 & 0.9443 \\
%     Chinese (ZHO) & Restaurant & 0.7205 & 0.5336 & 0.8705 \\
%     \midrule
%     \textbf{Average} & \textbf{All Tasks} & \textbf{0.5916} & \textbf{0.3074} & \textbf{1.1957} \\
%     \bottomrule
%     \end{tabular}

%   }
%   \caption{Performance results for \textbf{Full Parameter Fine-tuning} (Qwen-2.5-1.5B)}
%   \label{tab:full_ft_results}
% \end{table}

% While effective for English, this method exhibits a significant performance collapse in low-resource and specialized domains. Even getting scores lower than mmBERT. This indicates that the optimal amount of hyperparameters has to he chosen in order to fight this problem.
% \section{Conclusion and Limitations}
% In this work, we developed an encoder-decoder framework for multilingual dimABSA. Our experiments prove that while traditional encoders like mmBERT provide a stable baseline, LLMs with LoRA adaptation or full-fine tuning  are superior for capturing complex affective nuances. We identified the 'Stability-Plasticity' trade-off, where full fine-tuning leads to catastrophic forgetting of low-resource languages, while LoRA maintains multilingual integrity."

% In this work, we developed and evaluated a comprehensive framework for Multilingual Dimensional Aspect-Based Sentiment Analysis (dimABSA) across ten diverse task domains. Our "dual-track" investigation demonstrates a significant paradigm shift in performance when moving from traditional bidirectional encoders (\texttt{mmBERT}) to modern auto-regressive Large Language Models (\texttt{Qwen-2.5-1.5B}).
\section{Conclusion}
We evaluated encoder- and decoder-based approaches to multilingual dimABSA across ten domains. LoRA-tuned Qwen2.5-1.5B achieves the best overall performance (mean $RMSE_{VA}=1.14$, $\sigma\leq0.06$ across three seeds), outperforming full fine-tuning and head-only tuning. The central finding is that adapting only 1.18\% of parameters via LoRA avoids catastrophic forgetting while delivering strong cross-lingual transfer, particularly on low-resource Cyrillic-script subsets. Parameter-efficient fine-tuning of compact LLMs is a practical and robust strategy for multilingual dimensional sentiment analysis.

% Our experiments identified the \textbf{Stability-Plasticity trade-off} as the primary challenge in multilingual VA regression. While full parameter fine-tuning provides the model with maximum task-specific plasticity, it simultaneously triggers catastrophic forgetting of pre-trained cross-lingual priors, particularly in low-resource and specialized domains such as Tatar and Japanese Finance. We find that LoRA serves as the "golden middle" for this task; by adapting only 1.18\% of the backbone's parameters, we achieved the necessary specialization for regression while preserving the model's underlying linguistic integrity.
% These results suggest that the future of dimensional sentiment analysis lies in the parameter-efficient adaptation of large-scale foundations, enabling unified models that bridge the gap between high-resource and low-resource linguistic communities.
\section{Limitations}
Despite the competitive performance achieved by our \texttt{LoRA-adapted} framework, we identify several key limitations that warrant further investigation.

\paragraph{Hardware and Scaling Constraints} 
The primary bottleneck of this study was the computational environment. Our experiments were restricted to NVIDIA RTX 2080 Ti GPUs with 11GB of VRAM, which dictated the selection of the 1.5B parameter variant of \texttt{Qwen2.5}. While this model provides an excellent performance-to-size ratio, existing literature suggests that larger variants (e.g., 7B or 14B) possess significantly higher native reasoning capabilities for low-resource and distant languages

\paragraph{Linguistic Bottlenecks in Low-Resource Domains} 
A persistent resource disparity remains evident in our results. Even with optimal fine-tuning, the model’s performance on \textbf{Tatar (TAT)} and \textbf{Japanese Finance} remained significantly lower than on English and Chinese benchmarks. This suggests that the underlying pre-training distribution of the backbone model still heavily favors Indo-European and East Asian linguistic structures, leaving minority languages with a lower "performance floor"

\paragraph{Logical Semantics and Sentiment Flipping}
Qualitative analysis revealed that our system still struggles with complex linguistic structures such as contrastive conjunctions and long-range negation. The "Sentiment Flip" error observed in Table~\ref{tab:case_study} highlights that the model occasionally relies on local keywords (e.g., ``heaven'') rather than global sentence logic. This indicates a need for more sophisticated prompt engineering or specific "logic-aware" pre-training objectives to ensure models can handle contrastive affective shifts.

\bibliography{custom}

% \newpage

\appendix

\section{Qualitative Error Analysis}
\label{sec:appendix_error_analysis}
To understand the model's failure modes, we performed a qualitative analysis of high-error predictions. A primary issue identified is the \textbf{Sentiment Flip} in contrastive sentences. As we can see in the table \ref{tab:case_study}

\begin{table}[H]
  \centering
  \small
  \renewcommand{\arraystretch}{1.3} % Adds vertical padding 
  \begin{tabularx}{\columnwidth}{l X} % l = left-aligned, X = auto-wrap
    \toprule
    \textbf{Category} & \textbf{Details} \\
    \midrule
    \textbf{Sentence} & \textit{``Unfortunately, the waiter did sell the shrimp and grits with the additional crab as heaven in his mouth, but that it was not.''} \\
    \textbf{Aspect}   & \textit{shrimp and grits} \\
    \midrule
    \textbf{Gold (V, A)} & (1.88, 5.70) \\
    \textbf{Pred (V, A)} & (7.88, 5.93) \\
    \midrule
    \textbf{Observation} & \textbf{Sentiment Flip}: The model was distracted by the local positive keyword ``heaven'' and failed to process the global negation provided by ``Unfortunately'' and ``but that it was not.'' \\
    \bottomrule
  \end{tabularx}
  \caption{Case Study: Qualitative analysis of a Valence prediction error.}
  \label{tab:case_study}
\end{table}
The performance ceiling of the encoder-based architectures was happening exclusively due to this aspect of the sentences. There were some ideas to include the opinion or sentiment(positive/negative) but our LLM inference attempts couldn't produce useful results. \\
Finally, there was a problem of catastrophic forgetting in the full-fine-tuning process: Table~\ref{tab:full_ft_best}
\begin{table}[H]
  \resizebox{\columnwidth}{!}{
    \begin{tabular}{llccc}
    \toprule
    \textbf{Language} & \textbf{Domain} & \textbf{PCC\_V} $\uparrow$ & \textbf{PCC\_A} $\uparrow$ & \textbf{RMSE\_VA} $\downarrow$ \\
    \midrule
    English (ENG) & Laptop & 0.7954 & 0.4602 & 1.0376 \\
    English (ENG) & Restaurant & 0.8461 & 0.5775 & 0.9365 \\
    Japanese (JPN) & Finance & 0.4173 & -0.0074 & 1.2744 \\
    Japanese (JPN) & Hotel & 0.8614 & 0.5826 & 0.6558 \\
    Russian (RUS) & Restaurant & 0.7000 & 0.2288 & 1.3422 \\
    Tatar (TAT) & Restaurant & 0.1266 & 0.0628 & 1.6953 \\
    Ukrainian (UKR) & Restaurant & 0.5760 & 0.1441 & 1.5004 \\
    Chinese (ZHO) & Finance & 0.4355 & 0.1041 & 1.0171 \\
    Chinese (ZHO) & Laptop & 0.6143 & 0.2558 & 0.8799 \\
    Chinese (ZHO) & Restaurant & 0.7055 & 0.4618 & 0.8504 \\
    \midrule
    \textbf{Average} & \textbf{All Tasks} & \textbf{0.6078} & \textbf{0.2870} & \textbf{1.1190} \\
    \bottomrule
    \end{tabular}
  }
\caption{Performance results for \textbf{Full Parameter Fine-tuning} (Qwen-2.5-1.5B) using the optimal checkpoint at Epoch 6.}
  \label{tab:full_ft_best}
\end{table}
While effective for English, this method exhibits a significant performance collapse in low-resource and specialized domains. Even scoring lower than mmBERT. This indicates that the optimal amount of hyperparameters has to be chosen in order to fight this problem.
\begin{figure}[H]
\centering
\begin{tcolorbox}[colback=gray!5, colframe=gray!50, title=Dataset Example (JSONL)]
\begin{lstlisting}[language=json, basicstyle=\small\ttfamily, breaklines=true]
{
  "ID": "lap26_aspect_va_test_10",
  "Text": "For my purposes, the addition of a standard HDMI vice last years mini-HDMI is an improvement requiring no adapter",
  "Aspect_VA": [{
    "Aspect": "adapter",
    "VA": "6.50\#6.67"
  },
  {
    "Aspect": "standard HDMI",
    "VA": "7.00\#7.25"
  }]
}
\end{lstlisting}
\end{tcolorbox}
\caption{A formatted example of a single entry}
\label{fig:json_example}
\end{figure}
\section{Dataset data}
\begin{table}[H]
  \centering
    \small
  
  \resizebox{\columnwidth}{!}{
    \begin{tabular}{llrrr}
    \toprule
    \textbf{Language} & \textbf{Domain} & \textbf{Train} & \textbf{Dev} & \textbf{Test} \\
    \midrule
    English & Laptop/Rest. & 6,360 & 400 & 2,000 \\
    Chinese & Lap/Rest/Fin. & 10,540 & 800 & 2,842 \\
    Russian & Restaurant & 1,240 & 56 & 1,072 \\
    Tatar   & Restaurant & 1,240 & 56 & 1,072 \\
    Ukrainian & Restaurant & 1,240 & 56 & 1,072 \\
    Japanese & Finance & 1,024 & 200 & 800 \\
    Japanese & Hotel & 1,600 & 200 & 800 \\
    \bottomrule
    \end{tabular}
  }
\caption{Statistics of dimABSA shared task datasets.}
  \label{tab:dataset_stats}
\end{table}
% \begin{table}[H]
%   \centering
%   \footnotesize % Makes the text smaller
%   \begin{tabular}{lrr}
%     \toprule
%     \textbf{Language Group} & \textbf{Total Samples} & \textbf{\% of Global} \\
%     \midrule
%     English (ENG) & 8,760  & 29.15\% \\
%     Chinese (ZHO) & 14,182 & 47.20\% \\
%     Cyrillic (RUS, TAT, UKR)* & 7,104  & 23.65\% \\
%     \midrule
%     \textbf{Total Dataset} & \textbf{30,046} & \textbf{100.00\%} \\
%     \bottomrule
%   \end{tabular}
%   \centerline{\scriptsize *Each Cyrillic language represents exactly 7.88\% of the global data.}
%  \caption{Language distribution across the global dataset}
%   \label{tab:compact_stats}
% \end{table}
\begin{table}[H]
  \centering
  \resizebox{\columnwidth}{!}{ % This ensures the table stays within margins
    \begin{tabular}{lrr}
      \toprule
      \textbf{Language Group} & \textbf{Total Samples} & \textbf{Global \%} \\
      \midrule
      English (ENG) & 8,760  & 29.15\% \\
      Chinese (ZHO) & 14,182 & 47.20\% \\
      Cyrillic (RUS, TAT, UKR)* & 7,104  & 23.65\% \\
      \midrule
      \textbf{Total Dataset} & \textbf{30,046} & \textbf{100.00\%} \\
      \bottomrule
    \end{tabular}
  }
  \vspace{2pt}
  \centerline{\scriptsize *Each Cyrillic language represents exactly 7.88\% of the global data.}
\caption{Language distribution across the global dataset (Total $N=30,046$).}
  \label{tab:compact_stats}
\end{table}
\begin{table}[H]
  \centering
  \resizebox{\columnwidth}{!}{ % This ensures it never bleeds into the margin
    \begin{tabular}{lllc}
    \toprule
    \textbf{Dataset} & \textbf{Language} & \textbf{Original Scale} & \textbf{Size} \\
    \midrule
    EmoBank & English & 1.0 - 5.0 & 10,062 \\
    Facebook Posts & English & 1.0 - 9.0 & 2,895 \\
    SIGHAN TrainSet1 & Chinese & 1.0 - 9.0 & 6,000 \\
    \midrule
    \textbf{Total Added} & \textbf{Mixed} & \textbf{Scaled to 1-9} & \textbf{18,957} \\
    \bottomrule
    \end{tabular}
  }
  \caption{External affective datasets used for intermediate pre-training.}
  \label{tab:external_data}
\end{table}
\section{Ablated Hyperparameters}
\begin{table}[H]
\small
  \centering
  \begin{tabular}{ll}
  \toprule
  \textbf{Parameter} & \textbf{Value} \\
  \midrule
  Model & \texttt{jhu-clsp/mmBERT-base} \\
  Batch Size & 64 \\
  Grad. Accumulation & 1 \\
  Learning Rates & 1e-5, 2e-5, 1e-4 \\
  Epochs & 1, 2, 3, 4 \\
  \bottomrule
  \end{tabular}
  \caption{Hyperparameter search space for mmBERT fine-tuning.}
  \label{tab:params}
\end{table}



\section{Inference results for models}
\begin{table}
\small
  \centering
  \resizebox{\columnwidth}{!}{
    \begin{tabular}{l|ccc|ccc}
    \toprule
    \textbf{Task} & \multicolumn{3}{c|}{\textbf{Zero-Shot}} & \multicolumn{3}{c}{\textbf{Few-Shot (k=4)}} \\
    & $PCC_V \uparrow$ & $PCC_A \uparrow$ & $RMSE \downarrow$ & $PCC_V \uparrow$ & $PCC_A \uparrow$ & $RMSE \downarrow$ \\
    \midrule
    Eng. Laptop & 0.4673 & -0.0860 & 2.1429 & 0.7368 & 0.3631 & 1.4008 \\
    Eng. Rest. & 0.5586 & -0.0300 & 1.8112 & 0.7811 & 0.3481 & 1.3944 \\
    Jpn. Finance & 0.3477 & 0.0962 & 2.8303 & 0.5429 & 0.1158 & 2.1442 \\
    Jpn. Hotel & 0.6214 & 0.0166 & 1.9282 & 0.8510 & 0.3565 & 1.7401 \\
    Rus. Rest. & 0.5418 & 0.1114 & 2.2030 & 0.7754 & 0.2226 & 1.7451 \\
    Tat. Rest. & 0.1044 & 0.0628 & 2.5857 & 0.1602 & 0.1204 & 2.4319 \\
    Ukr. Rest. & 0.4275 & 0.0799 & 2.3704 & 0.6603 & 0.2421 & 1.8754 \\
    Zho. Finance & 0.4579 & 0.0300 & 2.6308 & 0.6104 & 0.2156 & 2.1156 \\
    Zho. Laptop & 0.3941 & 0.0572 & 2.3916 & 0.6885 & 0.3337 & 1.9058 \\
    Zho. Rest. & 0.4942 & -0.0697 & 2.1780 & 0.7053 & 0.4481 & 1.8196 \\
    \midrule
    \textbf{Average} & \textbf{0.4415} & \textbf{0.0268} & \textbf{2.3072} & \textbf{0.6512} & \textbf{0.2766} & \textbf{1.8573} \\
    \bottomrule
    \end{tabular}
  }
  \caption{Comparison of Zero-Shot and Few-Shot (k=4) performance across all tasks. Metrics include Pearson Correlation ($PCC_V, PCC_A$) and $RMSE_{VA}$.}
  \label{tab:icl_results}
\end{table}






\begin{table}
\small
  \centering
  \resizebox{\columnwidth}{!}{
    \begin{tabular}{lccc}
    \toprule
    \textbf{LR} & \textbf{Epochs} & \textbf{Dev RMSE} & \textbf{Test ENG RMSE} \\
    \midrule
    1e-5 & 1 & 1.1223 & 1.5034 \\
    1e-5 & 2 & 1.0414 & 1.4013 \\
    1e-5 & 3 & 0.9861 & 1.3140 \\
    1e-5 & 4 & 0.9621 & 1.3068 \\
    % 1e-5 (rmse-va loss) & 4 & 0.9849 & 1.2965 \\
    1e-4 & 4 & 0.9409 & 1.3113 \\
    \bottomrule
    \end{tabular}
  }
  \caption{Hyperparameter tuning results and RMSE performance for mmBERT models.}
  \label{tab:hyperparameter_tuning}
\end{table}

\begin{table}[H]
\small
  \centering
  \label{tab:lora_params}
  \begin{tabular}{lc}
  \toprule
  \textbf{Parameter} & \textbf{Value} \\
  \midrule
  Rank ($r$) & 16 \\
  Alpha ($\alpha$) & 32 \\
  Dropout & 0.05 \\
  Target Modules & All Linear Layers \\
  \bottomrule
  \end{tabular}
  \caption{LoRA configuration parameters for the Qwen-2.5-1.5B backbone.}\label{tab:lora}
\end{table}
% \begin{table}[H]
%   \centering
%   \footnotesize % Makes the text smaller
%   \begin{tabular}{lrr}
%     \toprule
%     \textbf{Language Group} & \textbf{Total Samples} & \textbf{\% of Global} \\
%     \midrule
%     English (ENG) & 8,760  & 29.15\% \\
%     Chinese (ZHO) & 14,182 & 47.20\% \\
%     Cyrillic (RUS, TAT, UKR)* & 7,104  & 23.65\% \\
%     \midrule
%     \textbf{Total Dataset} & \textbf{30,046} & \textbf{100.00\%} \\
%     \bottomrule
%   \end{tabular}
%   \centerline{\scriptsize *Each Cyrillic language represents exactly 7.88\% of the global data.}
%  \caption{Language distribution across the global dataset}
%   \label{tab:compact_stats}
% \end{table}
% \begin{figure}[H]
%     \centering
%     \resizebox{\columnwidth}{!}{
%         \begin{tikzpicture}
%             \begin{axis}[
%                 title={\textbf{Performance Trajectories: Qwen-2.5-1.5B Full Fine-Tuning}},
%                 xlabel={Training Epochs},
%                 ylabel={RMSE VA Score},
%                 xmin=1, xmax=6,
%                 ymin=0.6, ymax=2.0,
%                 xtick={1,2,3,4,5,6},
%                 ytick={0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0},
%                 legend pos=outer north east,
%                 legend cell align={left},
%                 grid=both,
%                 grid style={line width=.1pt, draw=gray!10},
%                 major grid style={line width=.2pt, draw=gray!20},
%                 width=12cm,
%                 height=8cm,
%                 mark size=2pt
%             ]

%             % 1. English Laptop (Benchmark)
%             \addplot[color=blue, mark=square*, line width=1pt] coordinates {
%                 (1, 1.2336) (2, 1.1594) (3, 1.0985) (4, 1.0346) (5, 1.0426) (6, 1.0376)
%             };
%             \addlegendentry{English Laptop}

%             % 2. Japanese Hotel (Strongest Performance)
%             \addplot[color=teal, mark=triangle*, line width=1pt] coordinates {
%                 (1, 0.8711) (2, 0.8408) (3, 0.7460) (4, 0.6550) (5, 0.6731) (6, 0.6558)
%             };
%             \addlegendentry{Japanese Hotel}

%             % 3. Tatar Restaurant (Low Resource Bottleneck)
%             \addplot[color=red, mark=diamond*, line width=1pt] coordinates {
%                 (1, 1.9577) (2, 1.8659) (3, 1.7744) (4, 1.7362) (5, 1.7168) (6, 1.6953)
%             };
%             \addlegendentry{Tatar Restaurant}

%             % 4. Global Average (Consolidated Trend)
%             \addplot[color=black, mark=*, line width=1.5pt, dashed] coordinates {
%                 (1, 1.3609) (2, 1.2833) (3, 1.2055) (4, 1.1557) (5, 1.1352) (6, 1.1190)
%             };
%             \addlegendentry{\textbf{Average (10 Tasks)}}

%             \end{axis}
%         \end{tikzpicture}
%     }
%     \caption{RMSE VA progression over 6 training epochs for the full fine-tuning configuration. The dashed black line illustrates the steady global convergence, while specific task trajectories highlight the variance between high-resource and low-resource (Tatar) domains.}
%     \label{fig:full_ft_trajectories}
% \end{figure}
\section{Full Fine Tune}
\begin{figure}[H]
    \centering
    \resizebox{\columnwidth}{!}{
        \begin{tikzpicture}
            \begin{axis}[
                title={\textbf{Cross-Lingual Convergence: All Task Trajectories}},
                xlabel={Training Epochs},
                ylabel={RMSE VA Score},
                xmin=1, xmax=6,
                ymin=0.6, ymax=2.0,
                xtick={1,2,3,4,5,6},
                ytick={0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0},
                legend pos=outer north east,
                legend cell align={left},
                legend style={font=\tiny},
                grid=both,
                grid style={line width=.1pt, draw=gray!10},
                major grid style={line width=.2pt, draw=gray!20},
                width=12cm,
                height=9cm,
                mark size=1.5pt
            ]

            % --- ENGLISH ---
            \addplot[color=blue, mark=square*] coordinates {
                (1, 1.2336) (2, 1.1594) (3, 1.0985) (4, 1.0346) (5, 1.0426) (6, 1.0376)
            };
            \addlegendentry{Eng-Laptop}

            \addplot[color=cyan, mark=square] coordinates {
                (1, 1.1857) (2, 1.1078) (3, 1.0210) (4, 0.9311) (5, 0.9425) (6, 0.9365)
            };
            \addlegendentry{Eng-Restaurant}

            % --- JAPANESE ---
            \addplot[color=teal, mark=triangle*] coordinates {
                (1, 1.6192) (2, 1.5026) (3, 1.4056) (4, 1.3491) (5, 1.3026) (6, 1.2744)
            };
            \addlegendentry{Jpn-Finance}

            \addplot[color=emerald, mark=triangle] coordinates {
                (1, 0.8711) (2, 0.8408) (3, 0.7460) (4, 0.6550) (5, 0.6731) (6, 0.6558)
            };
            \addlegendentry{Jpn-Hotel}

            % --- CHINESE ---
            \addplot[color=green!70!black, mark=diamond*] coordinates {
                (1, 1.3481) (2, 1.2454) (3, 1.1541) (4, 1.1013) (5, 1.0422) (6, 1.0171)
            };
            \addlegendentry{Zho-Finance}

            \addplot[color=lime!70!black, mark=diamond] coordinates {
                (1, 1.1221) (2, 1.0458) (3, 0.9784) (4, 0.9578) (5, 0.9008) (6, 0.8799)
            };
            \addlegendentry{Zho-Laptop}

            \addplot[color=olive, mark=pentagon*] coordinates {
                (1, 0.9491) (2, 0.9291) (3, 0.8935) (4, 0.8546) (5, 0.8551) (6, 0.8504)
            };
            \addlegendentry{Zho-Restaurant}

            % --- CYRILLIC / LOW RESOURCE ---
            \addplot[color=orange, mark=o] coordinates {
                (1, 1.6134) (2, 1.4985) (3, 1.4114) (4, 1.3848) (5, 1.3557) (6, 1.3422)
            };
            \addlegendentry{Rus-Restaurant}

            \addplot[color=violet, mark=x] coordinates {
                (1, 1.7093) (2, 1.6377) (3, 1.5719) (4, 1.5524) (5, 1.5211) (6, 1.5004)
            };
            \addlegendentry{Ukr-Restaurant}

            \addplot[color=red, mark=asterisk, line width=1pt] coordinates {
                (1, 1.9577) (2, 1.8659) (3, 1.7744) (4, 1.7362) (5, 1.7168) (6, 1.6953)
            };
            \addlegendentry{Tat-Restaurant}

            \end{axis}
        \end{tikzpicture}
    }
    \caption{Trajectory analysis of all 10 task domains over 6 epochs. The visualization highlights that high-resource domains (English, Japanese-Hotel) achieve early stability, while low-resource languages (Tatar, Ukrainian) maintain a steeper learning gradient throughout the training process.}
    \label{fig:all_trajectories}
\end{figure}
\section{Baseline Prompts}
\begin{figure}[H]
    \centering
    \begin{tcolorbox}[colback=gray!5, title=Zero-Shot Template]
    \small
    \begin{verbatim}
    You are a sentiment analysis system. 
    Predict two scores (1.00-9.00):
    Valence (V): positivity.
    Arousal (A): intensity.
    Respond ONLY in format V#A.
    Text: {text} | Aspect: {aspect} | V#A:
    \end{verbatim}
    \end{tcolorbox}
    \caption{Zero-shot prompt for Qwen2.5-1.5B.}
    \label{fig:zsprompt}
\end{figure}

\begin{figure}[H]
    \centering
    \begin{tcolorbox}[colback=gray!5, title=Few-Shot Template]
    \small
    \begin{verbatim}
    [Instructions as above]
    Examples:
    Text: The touchscreen works very well | 
    Aspect: touchscreen | V#A: 7.80#7.60
    Text: The hardware... is really cheap | 
    Aspect: hardware... | V#A: 3.25#7.50
    ...
    Text: {text} | Aspect: {aspect} | V#A:
    \end{verbatim}
    \end{tcolorbox}
    \caption{Few-shot prompt template for Qwen2.5-1.5B.}
    \label{fig:fsprompt}
\end{figure}




% tabel for encoder results 
\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{@{}llcccccccccccl@{}}
\toprule
\textbf{Method} & \textbf{Setup} & \multicolumn{2}{c}{\textbf{English}} & \multicolumn{2}{c}{\textbf{Japanese}} & \textbf{Russian} & \textbf{Tatar} & \textbf{Ukrainian} & \multicolumn{3}{c}{\textbf{Chinese}} & \textbf{Avg.} \\ \cmidrule(lr){3-4} \cmidrule(lr){5-6} \cmidrule(lr){10-12}
 &  & Laptop & Rest. & Finance & Hotel & Rest. & Rest. & Rest. & Finance & Laptop & Rest. &  \\ \midrule
\multirow{4}{*}{Encoder} 
& Full FT (mmBERT-base) & 1.31 & 1.34 & 1.12 & 0.75 & 1.60 & 1.89 & 1.63 & 0.67 & 0.89 & 1.05 & 1.22 \\
& Full FT (XLM-R-base) & 1.49 & 1.38 & 1.17 & 0.90 & 1.78 & 2.22 & 1.83 & 0.67 & 1.01 & 1.14 & 1.36 \\
& Full FT (XLM-R-large) & 1.36 & 1.28 & 1.13 & 0.75 & 1.78 & 2.14 & 1.81 & 0.62 & 0.86 & 1.00 & 1.27 \\
& Full FT (mDeBERTa-v3-base) & 2.13 & 2.08 & 1.42 & 1.33 & 2.17 & 2.22 & 2.17 & 0.87 & 1.28 & 1.36 & 1.70 \\ \bottomrule
\end{tabular}
}
\caption{Comparison of RMSE\_VA across different languages and domains for bidirectional encoder baselines. Lower is better.}
\label{tab:results_encoders_all}
\end{table*}https://box.skoltech.ru/index.php/s/raUdSKzhPGDmVuE?path=%2FLecture_5

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{@{}llcccccccccccl@{}}
\toprule
\textbf{Method} & \textbf{Setup} & \multicolumn{2}{c}{\textbf{English}} & \multicolumn{2}{c}{\textbf{Japanese}} & \textbf{Russian} & \textbf{Tatar} & \textbf{Ukrainian} & \multicolumn{3}{c}{\textbf{Chinese}} & \textbf{Avg.} \\ \cmidrule(lr){3-4} \cmidrule(lr){5-6} \cmidrule(lr){10-12}
 &  & Laptop & Rest. & Finance & Hotel & Rest. & Rest. & Rest. & Finance & Laptop & Rest. &  \\ \midrule
\multirow{4}{*}{LoRA} 
& Seed 7    & 1.07 & 0.99 & 1.31 & 0.64 & 1.24 & 1.74 & 1.49 & 1.15 & 0.90 & 0.85 & 1.14 \\
& Seed 42   & 1.06 & 1.00 & 1.28 & 0.65 & 1.30 & 1.72 & 1.52 & 1.03 & 0.86 & 0.83 & 1.13 \\
& Seed 1337 & 1.06 & 0.97 & 1.35 & 0.66 & 1.36 & 1.74 & 1.53 & 1.09 & 0.88 & 0.84 & 1.15 \\ \cmidrule(l){2-13}
& \textbf{Mean $\pm$ SD} & \textbf{1.06$\pm$.01} & \textbf{0.99$\pm$.01} & \textbf{1.31$\pm$.03} & \textbf{0.65$\pm$.01} & \textbf{1.30$\pm$.06} & \textbf{1.73$\pm$.01} & \textbf{1.51$\pm$.02} & \textbf{1.09$\pm$.06} & \textbf{0.88$\pm$.02} & \textbf{0.84$\pm$.01} & \textbf{1.14$\pm$.01} \\ \bottomrule
\end{tabular}
}
\caption{Statistical validation of the LoRA-tuned Qwen2.5-1.5B model across three independent random seeds. Results reported in $RMSE_{VA}$.}
\label{tab:lora_seeds}
\end{table*}
\begin{figure}[htbp]
    \centering
    \includegraphics[width=0.9\linewidth]{Violin_Plot.pdf}
    \caption{Violin plots of emotion dimensions. The width of the blue DimABSA violins at the extremes (low and high scores) illustrates why models trained on the neutral-heavy EmoBank (orange) fail to generalize to the target test set.}
    \label{fig:violin_dist}
\end{figure}
\end{document}