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\title{SokraTUM at SemEval-2026 Task 3: A hybrid cascade of Label Distribution Learning, RAG supported generative extraction and contrastive metric learning for dimensional sentiment analysis}

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\author{
 \textbf{Denis Laschenko*\textsuperscript{1}} \hfill
 \textbf{Albert Korotyk*\textsuperscript{1}} \hfill
\\
 \textsuperscript{1}Technical University of Munich,
 % \textsuperscript{2}Affiliation 2,
 % \textsuperscript{3}Affiliation 3,
 % \textsuperscript{4}Affiliation 4,
 % \textsuperscript{5}Affiliation 5
\\
 \texttt{(\href{mailto:d.laschenko@tum.de}{d.laschenko}, \href{mailto:albert.korotyk@tum.de}{albert.korotyk})@tum.de}
}

\begin{document}
\maketitle
\begin{abstract}
The Dimensional ABSA (DimABSA) shared task extends traditional aspect-based sentiment analysis from categorical polarity to continuous valence--arousal (VA) prediction. We present our system for all three subtasks: Dimensional Aspect Sentiment Regression (DimASR), Dimensional Aspect Sentiment Triplet Extraction (DimASTE), and Dimensional Aspect Sentiment Quad Prediction (DimASQP).

Due to the cascading nature of the different subtasks, we built a modular interlocking pipeline that uses classical Machine Learning and NLP methods.

Experiments across domains show consistent gains in regression accuracy and structured extraction performance. Our results demonstrate the effectiveness of distribution-aware regression, retrieval-augmented generation, and contrastive prototype learning for dimensional sentiment analysis.
\end{abstract}

\section{Introduction}
Aspect-Based Sentiment Analysis (ABSA) typically models sentiment polarity as discrete labels (positive, negative, neutral) \cite{pontiki-etal-2014-semeval} \cite{zhang2022surveyaspectbasedsentimentanalysis}.  However, human affect is inherently continuous. The valence--arousal (VA) framework \cite{10.1037/h0077714} represents sentiment along two real-valued dimensions, capturing both polarity and intensity. The Dimensional ABSA (DimABSA) shared task  integrates this framework into structured sentiment analysis \citep{lee2026dimabsabuildingmultilingualmultidomain,yu-etal-2026-semeval}.

Consider the following restaurant review: \textit{"average to good thai food, but terrible delivery."} The Dimensional ABSA (DimABSA) framework captures these differences using continuous Valence-Arousal (VA) scores. For example, the \textbf{thai food} is assigned (Valence: 6.75, Arousal: 6.38) to represent moderately high-energy positive satisfaction, while the \textbf{delivery} receives (Valence: 2.88, Arousal: 6.62) to represent high-energy negative frustration. This example illustrates that modeling sentiment as a continuous dimensional space provides a richer understanding of user experiences compared to discrete labels (A complete, structured JSONL input-output example is provided in Appendix D).

The task consists of three subtasks \cite{yu-etal-2026-semeval}: 
(1) \textbf{DimASR}, predicting VA scores for given aspects; 
(2) \textbf{DimASTE}, extracting (Aspect, Opinion, VA) triplets; and 
(3) \textbf{DimASQP}, extracting (Aspect, Category, Opinion, VA) quadruplets. 
Compared to categorical ABSA, these tasks introduce additional challenges: subjective regression targets, span instability in generative extraction, and category imbalance.
We address these challenges with task-specific designs. For DimASR, we apply Label Distribution Learning (LDL) \cite{geng2016labeldistributionlearning} to model annotation uncertainty and optimize Concordance Correlation Coefficient (CCC) loss to better capture correlation and scale agreement \cite{7472669}. For DimASTE, we combine Parameter-Efficient Fine-Tuning (PEFT) \cite{hu2021loralowrankadaptationlarge} with Retrieval-Augmented In-Context Learning (RAG-ICL) \citep{lewis2021retrievalaugmentedgenerationknowledgeintensivenlp,dong-etal-2024-survey} to enhance large language model predictions and apply fuzzy grounding \cite{ratcliff1988pattern} to align generated spans with the input text. For DimASQP, we use Supervised Contrastive Learning (SupCon) \cite{khosla2021supervisedcontrastivelearning} to structure the category embedding space and perform nearest centroid inference for robust prediction under few-shot and imbalanced settings.

Our approach provides a unified yet flexible pipeline for structured dimensional sentiment analysis and achieves competitive performance across subtasks.

\begin{comment}
\section{Backgound}
\subsection{Task Description}

The SemEval-2026 Task 3 evaluates systems on Dimensional Aspect-Based Sentiment Analysis (DimABSA) across three hierarchical subtasks. With $S = (w_1,w_2,...,w_n)$ denote a source textual sequence (e.g., a customer review), the subtasks are defined as follows:
\paragraph{Dimensional Aspect Sentiment Regression:}The System receives a tuple $(S,A)$ where $A=\{a_1,...,a_k\}$ is a predefined set of textspans present in $S$ indicating the target of the opinion, the 'aspect' term. The objective is to learn a regression mapping $f_{ASR}: (S,A) \rightarrow \mathbf{y}_i$. The output $\mathbf{y}_i = (v_i, a_i)$ is the continuous Valence-Arousal score, constrained to a bounded real-valued vector where $v_i, a_i \in [1.00,9.00]$ rounded to two decimal places.
\paragraph{Dimensional Aspect Sentiment Triplet Extraction:}The system receives solely the sequence $S$. The objective is to learn an extraction function $f_{ASTE}: S \rightarrow T$, where $T = \{(a_i, o_i, \mathbf{y}_i)\}_{i=1}^{m}$ represents the set of valid triplets. This formulation introduces $o_i$, defined as the corresponding opinion term (a sentiment-bearing text span) that explicitly modifies the extracted aspect $a_i$.
\paragraph{ Dimensional Aspect Sentiment Quad Prediction:}Expanding upon DimASTE, the system learns an extraction function $f_{ASQP}: S \rightarrow Q$, where $Q = \{(a_i, c_i, o_i, \mathbf{y}_i)\}_{i=1}^{p}$. This formulation integrates the aspect category $c_i \in C$, where $C$ is a predefined ontology of categorical labels. To be considered valid, $c_i$ must strictly conform to an Entity\#Attribute string format (e.g., FOOD\#QUALITY).

\subsection{Datasets}

We participate in the English track for both \textbf{laptop} and \textbf{restaurant} domains. The datasets consist of annotated review sentences with gold triplets or quadruplets, following the ABSA tradition of SemEval shared tasks \citep{pontiki-etal-2014-semeval}. The datasets feature 4076 samples for laptop and 2284 samples for restaurant.

The restaurant dataset contains multi-aspect restaurant reviews covering food quality, drinks, service, and ambience. The laptop dataset focuses on product reviews, including company, hardware, and performance-related categories. Both datasets include predefined category taxonomies and continuous VA annotations per aspect.

\subsection{Relation to Prior Work}

Traditional ABSA approaches focus on either aspect term extraction \citep{li2019unified} or sentiment classification \citep{sun-etal-2019-utilizing}. More recent work addresses unified triplet or quadruplet extraction \citep{zhang2021asqp, cai2021quadruple}, often using sequence tagging or generative modeling. However, these methods assume categorical sentiment labels.

DimABSA introduces two key challenges beyond prior work: (1) continuous sentiment regression instead of discrete classification, and (2) joint modeling of structured extraction with dimensional affect prediction.

Our work contributes to this emerging direction by combining distribution-aware regression, retrieval-augmented structured extraction, and contrastive category modeling within a unified pipeline for dimensional sentiment analysis.

% Current Aspect-Based Sentiment Analysis (ABSA) research typically focuses on identifying four key sentiment elements: \textit{aspect terms}, \textit{aspect categories}, \textit{opinion terms}, and \textit{sentiment polarity}. In this work, we introduce \textbf{DimABSA}, which extends traditional ABSA by replacing categorical sentiment polarity with continuous Valence-Arousal (VA) scores.
\end{comment}

\section{Background}
\subsection{Task Description}
The task evaluates systems on Dimensional ABSA across three hierarchical subtasks \cite{yu-etal-2026-semeval} given a source sequence $S=(w_1,...,w_n)$. \textbf{DimASR (Regression):} Given $S$ and predefined aspect terms $A=\{a_1,...,a_k\}$, the system learns a regression mapping $f_{ASR}:(S,A) \rightarrow y_i$, where $y_i=(v_i,a_i) \in [1.00,9.00]^2$ represents continuous Valence-Arousal (VA) scores. \textbf{DimASTE (Triplet Extraction):} The system learns $f_{ASTE}:S \rightarrow T$, extracting triplets $T=\{(a_i,o_i,y_i)\}$, introducing the opinion term $o_i$ that modifies $a_i$. \textbf{DimASQP (Quad Prediction):} The system learns $f_{ASQP}:S \rightarrow Q$, extracting quadruplets $Q=\{(a_i,c_i,o_i,y_i)\}$, integrating the aspect category $c_i \in C$ from a predefined ontology (e.g., \texttt{FOOD\#QUALITY}).

\subsection{Datasets and Prior Work}
We utilize the official English laptop (4,076 samples) and restaurant (2,284 samples) datasets \cite{lee2026dimabsabuildingmultilingualmultidomain}, which feature multi-aspect reviews and continuous VA annotations per aspect. While traditional ABSA \citep{pontiki-etal-2014-semeval} and recent unified extraction methods \citep{sun-etal-2019-utilizing} assume categorical labels, DimABSA introduces the novel challenge of continuous sentiment regression \cite{yu-etal-2026-semeval}. We address this transition by replacing standard classifiers with distribution-aware regression, retrieval-augmented generation, and contrastive prototype learning. Related work has shown that hybrid and retrieval-augmented architectures are effective for complex NLP pipelines \cite{shaghayeghkolli-etal-2025-hybrid,uyuk-etal-2024-crafting}, while large-scale social media analysis highlights the importance of modeling information dynamics in real-world data \cite{8712010}.

% For example, given the sentence ``The salads are fantastic.'', the extracted elements under the two settings would be:
% \begin{itemize}
%     \item \textbf{ABSA:} (salads, FOOD\#QUALITY, fantastic, Positive)
%     \item \textbf{DimABSA:} (salads, FOOD\#QUALITY, fantastic, 7.88\#7.75)
% \end{itemize}

% The specific elements involved in this task are defined as follows:
% \begin{description}
%     \item[Aspect Term:] A word or phrase indicating an opinion target (e.g., appetizer, waiter, battery, screen).
%     \item[Aspect Category:] An abstract or predefined category to which an aspect term belongs. It follows the format \texttt{Entity\#Attribute}, where the Entity (e.g., FOOD, SERVICE) and Attribute (e.g., PRICES, QUALITY) are selected from predefined lists \cite{pontiki2015semeval}.
%     \item[Opinion Term:] A sentiment-bearing word or phrase associated with a specific aspect term (e.g., great, terrible, satisfactory).
%     \item[Valence-Arousal (VA):] A pair of real-valued scores, each ranging from 1.00 to 9.00, rounded to two decimal places.
%     \begin{itemize}
%         \item \textbf{Valence:} Measures the degree of positivity or negativity.
%         \item \textbf{Arousal:} Measures the intensity of emotion.
%     \end{itemize}
%     A score of 1.00 indicates extremely negative valence or very low arousal, 9.00 indicates extremely positive valence or very high arousal, and 5.00 represents a neutral valence or medium arousal.
% \end{description}

% Based on these elements, we define three subtasks, each adapted from a traditional ABSA task to the dimensional sentiment paradigm.

% \subsection{Subtask Definitions}

% \subsubsection{Subtask 1: Dimensional Aspect Sentiment Regression (DimASR)}
% Given a text and one or more aspects, the goal is to predict a real-valued valence-arousal (VA) score for each aspect. This extends Aspect Sentiment Classification (ASC).

% \textbf{Input Format (JSON Lines):}
% \begin{lstlisting}
% {
%   "ID": "Unique identifier",
%   "Text": "A sentence expressing subjective opinions",
%   "Aspect": ["List of one or more target aspects"]
% }
% \end{lstlisting}

% \textbf{Output Format (JSON Lines):}
% \begin{lstlisting}
% {
%   "ID": "Unique identifier matching input",
%   "Aspect_VA": [
%     {
%       "Aspect": "Aspect name (case-sensitive)",
%       "VA": "V#A (e.g., 7.88#7.75)"
%     }
%   ]
% }
% \end{lstlisting}

% \subsubsection{Subtask 2: Dimensional Aspect Sentiment Triplet Extraction (DimASTE)}
% Given a text, the goal is to extract all $(A, O, \text{VA})$ triplets, where $A$ denotes an aspect term, $O$ an opinion term, and VA a valence-arousal score. This extends Aspect Sentiment Triplet Extraction (ASTE).

% \textbf{Input Format (JSON Lines):}
% \begin{lstlisting}
% {
%   "ID": "Unique identifier",
%   "Text": "A sentence expressing subjective opinions"
% }
% \end{lstlisting}

% \textbf{Output Format (JSON Lines):}
% \begin{lstlisting}
% {
%   "ID": "Unique identifier matching input",
%   "Triplet": [
%     {
%       "Aspect": "Aspect term (case-sensitive)",
%       "Opinion": "Opinion term (case-sensitive)",
%       "VA": "V#A score"
%     }
%   ]
% }
% \end{lstlisting}

% \subsubsection{Subtask 3: Dimensional Aspect Sentiment Quad Prediction (DimASQP)}
% Given a text, the goal is to extract all $(A, C, O, \text{VA})$ quadruplets, where $A$ denotes an aspect term, $C$ an aspect category, $O$ an opinion term, and VA a valence-arousal score. This extends Aspect Sentiment Quad Prediction (ASQP).

% \textbf{Output Format (JSON Lines) - Key Additions:}
% \begin{lstlisting}
% {
%   "ID": "Unique identifier matching input",
%   "Quadruplet": [
%     {
%       "Aspect": "Aspect term (case-sensitive)",
%       "Category": "ENTITY#ATTRIBUTE (UPPERCASE)",
%       "Opinion": "Opinion term (case-sensitive)",
%       "VA": "V#A score"
%     }
%   ]
% }
% \end{lstlisting}

% \section{Methodology}


% \subsection{Proposed Approaches}

% To address the challenges of the defined subtasks, we employ the following methodological strategies:

% \paragraph{Approaches for Subtask 1 (DimASR):}
% \begin{itemize}
%     \item \textbf{Label Distribution Learning (LDL):} Instead of treating V/A scores as scalar targets, we model them as Gaussian distributions. The loss function minimizes the KL-Divergence between the predicted distribution and the ground truth.
%     \item \textbf{Concordance Correlation Coefficient (CCC):} We utilize CCC Loss to force the model to learn the correlation and trend of the data, rather than just focusing on absolute values, which is crucial for regression tasks involving human sentiment.
% \end{itemize}

% \paragraph{Approaches for Subtask 2 (DimASTE):}
% \begin{itemize}
%     \item \textbf{RAG-ICL (Retrieval Augmented In-Context Learning):} We retrieve $k$ similar examples from the training set based on embedding similarity to guide the model during extraction.
%     \item \textbf{Fuzzy Grounding:} Large Language Models (LLMs) often hallucinate slight variations of words (e.g., extracting ``battery'' instead of ``battery life''). We implement a fuzzy grounding step in post-processing to snap these extractions back to valid terms found in the text.
% \end{itemize}

% \paragraph{Approaches for Subtask 3 (DimASQP):}
% \begin{itemize}
%     \item \textbf{Contrastive Loss (SupCon):} We replace a simple Softmax classifier with a Contrastive Loss mechanism. This creates an embedding space where similar categories cluster together, which is highly effective for ``Few-Shot'' scenarios or handling unbalanced categories.
%     \item \textbf{Nearest Centroid Inference:} To better adapt to the test set distribution, we employ prototype matching (Nearest Centroid) rather than relying on a frozen Softmax layer.
% \end{itemize}


\section{System Overview}
\subsection{Pipeline Architecture}
%\include{latex/image/pipeline}
We propose a modular cascading framework to address the three DimABSA subtasks \cite{yu-etal-2026-semeval}. Each subtask is modeled with a dedicated component tailored to its objective, while intermediate representations are reused when beneficial. Subtask~2 (triplet extraction) extracts aspect–opinion pairs and stets VA scores to 0\#0, Subtask~1 (VA regression) provides a standalone regression module predicting VA, and Subtask~3 (category prediction) extends extracted triplets with aspect categories.
\begin{figure*}[t]
    \centering
    \includegraphics[width=0.85\textwidth]{latex/graphics/Pipeline_ver1.png}
    \caption{Modular Cascading Pipeline}
    \label{fig:pipeline}
\end{figure*}
Our design separates continuous regression, generative extraction, and discrete category prediction to allow task-specific optimization while maintaining a coherent pipeline.

\subsection{Subtask 1: Dimensional Aspect Sentiment Regression}

We formulate valence and arousal prediction as a distribution learning problem rather than direct scalar regression. Given an input sequence, we format the aspect and the surrounding context using the standard RoBERTa tokenizer schema \cite{liu2019robertarobustlyoptimizedbert} (i.e., \texttt{<s> Aspect </s></s> Sentence </s>}). Instead of relying solely on the \texttt{<s>} (CLS) token, a pretrained RoBERTa encoder processes this input, and we concatenate the hidden states from the final four Transformer layers. We apply mean-pooling \cite{gao-etal-2021-simcse} across all non-padded tokens to produce a robust global contextual representation, $h_{pool}$. Two independent linear prediction heads ($W_v$ and $W_a$) then project this representation into logits ($z^v$, $z^a$) over $B$ discretized bins covering the interval $[1,9]$.

\subsubsection{Distributional Regression Objective
}

Instead of minimizing mean squared error, we model each gold VA score as a Gaussian-shaped label distribution over $B$ bins. The model predicts a discrete distribution via a softmax over the logits, and the final scalar prediction is deterministically computed as the mathematical expectation over the predicted bin centers.

To explicitly penalize scale shifts and capture trend alignment, we combine a Kullback-Leibler divergence objective ($\mathcal{L}_{LDL}$) \cite{geng2016labeldistributionlearning} with the Concordance Correlation Coefficient ($\text{CCC}$) \cite{7472669}, a standard metric in affective computing. (Formal mathematical definitions for both components are provided in Appendix A).

Because early validation indicated Arousal exhibits a higher variance and is significantly harder to predict than Valence, we explicitly up-weight the Arousal penalty in our final composite loss:
\vspace{-1mm}
\[
\mathcal{L} = \sum_{d \in \{v, a\}} w_d \Big( \mathcal{L}_{LDL}^{(d)} + \lambda(1 - \text{CCC}(y^{(d)}, \hat{y}^{(d)})) \Big)
\]
\vspace{-1mm}
where $y^{(d)}$ and $\hat{y}^{(d)}$ are the gold and predicted scalars for dimension $d$. We empirically set the correlation penalty $\lambda=0.5$ and the dimension weights to $w_v=1.0$ and $w_a=2.0$. This architecture ensures local distributional fidelity while forcing the model to prioritize the more challenging affective dimension.

% \[
% q_i = \frac{1}{Z} \exp\left(-\frac{(b_i - y)^2}{2\sigma^2}\right),
% \]

% where $b_i$ denotes the $i$-th bin center and $Z$ is a normalization constant. The model predicts a discrete distribution $p_i = \text{softmax}(z_i)$We optimize the Kullback-Leibler (KL) divergence between $q_i$ and $p_i$, denoted as

% \[
% \mathcal{L}_{LDL} = \sum_i q_i \log \frac{q_i}{p_i}.
% \]

% The final scalar prediction is computed as the expectation: $\hat{y} = \sum_i p_i b_i.$
% This formulation explicitly captures annotation uncertainty and reduces over-penalization of near-miss predictions.

% \subsubsection{Concordance Correlation Coefficient Loss}

% To explicitly penalize scale shifts and capture trend alignment, we augment the training objective with the Concordance Correlation Coefficient (CCC):

% \[
% \text{CCC}(y,\hat{y}) =
% \frac{2\rho\sigma_y\sigma_{\hat{y}}}
% {\sigma_y^2 + \sigma_{\hat{y}}^2 + (\mu_y - \mu_{\hat{y}})^2}.
% \]

% \[
% \mathcal{L} = \sum_{d \in \{v, a\}} w_d \Big( \mathcal{L}_{LDL}^{(d)} + \lambda(1 - \text{CCC}(y^{(d)}, \hat{y}^{(d)})) \Big)
% \]

% We optimize $\mathcal{L}_{CCC} = 1 - \text{CCC}.$
% The final objective is $\mathcal{L} = \mathcal{L}_{LDL} + \lambda \mathcal{L}_{CCC}$, where we empirically set $\lambda=0.5$. This combination ensures both distributional fidelity and correlation consistency. We further apply layer-wise learning rate decay, AdamW optimization, mixed precision training, and multi-seed ensembling for stability.

\subsection{Subtask 2: Dimensional Triplet Extraction}

We formulate the extraction phase of DimASTE as an instruction-following structured generation task. Given a source sentence, the model is prompted to output intermediate triplets utilizing a placeholder for the continuous values: $(\text{Aspect}, \text{Opinion}, 0\#0).$


We fine-tune an instruction-tuned Large Language Model (LLM) using parameter-efficient LoRA adaptation \cite{hu2021loralowrankadaptationlarge} under 4-bit quantization (QLoRA). Crucially, we intentionally offload the continuous VA prediction to the downstream DimASR regressor. Therefore, during the SFT phase \cite{qwen2025qwen25technicalreport}, the LLM is explicitly optimized only to strictly extract the categorical aspect and opinion spans from the input text. This decoupled design prevents the generative model from expending representational capacity on numerical regression---a task it inherently struggles with---while ensuring seamless integration with our dedicated Label Distribution Learning (LDL) module.


\subsubsection{Retrieval-Augmented In-Context Learning}
To improve structural consistency and reduce formatting hallucinations, we apply Retrieval-Augmented In-Context Learning (RAG-ICL) \cite{lewis2021retrievalaugmentedgenerationknowledgeintensivenlp, dong-etal-2024-survey} at inference. For each test sentence, we retrieve $k$ semantically similar training examples using embedding similarity and prepend them to the prompt as demonstrations. This dynamic prompting provides implicit structural guidance and significantly improves the LLM's extraction stability without requiring further weight updates.

\subsubsection{Fuzzy Span Grounding}
Despite explicit instruction tuning, generative models inherently suffer from span instability, frequently producing boundary hallucinations that do not verbatim match the source text. Because the DimASTE evaluation metrics require exact span extraction, we introduce a post-hoc fuzzy span grounding mechanism. 

If a generated span $s_g$ of length $n$ does not perfectly align with a substring in the source sentence $S$, we perform a dynamic sliding-window search. We extract contiguous candidate substrings $s_{sub}$ from $S$ with lengths ranging from $n-2$ to $n+2$. We then select the grounded span $s^*$ that maximizes the Ratcliff-Obershelp sequence similarity ratio (Gestalt Pattern Matching) \cite{ratcliff1988pattern}, defined as $s^* = \arg\max_{s_{sub} \in S_{window}} \text{Sim}_{RO}(s_g, s_{sub})$, where $S_{window}$ represents the set of extracted sliding-window chunks. Finally, we apply an empirical confidence threshold. If $\text{Sim}_{RO}(s_g, s^*) > 0.85$, the hallucinated prediction is replaced by the exact boundaries of $s^*$. This deterministic heuristic successfully recovers misaligned boundaries without modifying the underlying LLM weights.
\vspace{-2mm}
\subsection{Subtask 3: Dimensional Quad Prediction}

Subtask~3 extends triplets with aspect category prediction. Instead of a softmax classifier, we adopt Supervised Contrastive Learning (SupCon) \cite{gao-etal-2021-simcse} to learn a structured embedding space.

Each instance is explicitly encoded as a unified sequence string (e.g., \texttt{<s> Aspect </s> Opinion </s> Sentence </s>}) and mapped to an $L_2$-normalized embedding $z$. For a batch of embeddings, we optimize the SupCon loss \cite{khosla2021supervisedcontrastivelearning} (the formal mathematical definition is provided in Appendix A) to pull embeddings of the same class together while pushing apart different classes.

\subsubsection{Prototype-Based Inference}

After training, we compute class centroids $c_k = \frac{1}{|D_k|} \sum_{i \in D_k} z_i$, where $D_k$ contains embeddings of class $k$. At inference, prediction is performed via nearest centroid: $\hat{k} = \arg\max_k z \cdot c_k$.

\section{Experimental Setup}

\subsection{Data and Splits}
We utilize the official datasets \cite{lee2026dimabsabuildingmultilingualmultidomain} provided by the DimABSA shared task organizers for the \texttt{restaurant} and \texttt{laptop} domains (both English). To maximize the volume of training instances for the final evaluation phase, we concatenated the official training and development sets into a single consolidated dataset. 

From this consolidated data, we dynamically generated our own internal splits, allocating 90\% of the instances for model optimization and holding out 10\% for internal validation and early stopping. Crucially, to prevent data leakage during this split---a scenario where a single review sentence containing multiple aspects might inadvertently bridge the training and validation sets---we applied a Group Shuffle Split \cite{scikit} grouped by the raw source sentence. The official, unlabelled test set was kept strictly isolated and used exclusively for the final leaderboard submission. No external labeled datasets were incorporated.

\subsection{Preprocessing}
All sentences are lowercased and tokenized using the tokenizer corresponding to each pretrained backbone. To maintain consistency with our mathematical formulation, Subtask 1 inputs are treated as a sentence-pair regression task by constructing an auxiliary sentence from the aspect term \cite{sun-etal-2019-utilizing}. formatted using the standard RoBERTa schema (i.e., $x = [\texttt{<s>}, \text{Aspect}, \texttt{</s>}, \texttt{</s>}, \text{Text}, \texttt{</s>}]$).

For Subtasks 2 and 3, instruction-style prompts are constructed to enforce structured output formatting. Extracted spans are post-processed using our fuzzy span grounding heuristic based on the difflib sequence-matcher \cite{ratcliff1988pattern} to ensure exact metric matching with the source text. 

Valence and arousal scores are discretized into 9 evenly spaced bins covering the interval $[1,9]$ for Label Distribution Learning \cite{geng2016labeldistributionlearning}. During inference, final scalar predictions are computed as the mathematical expectation over the predicted bin distributions.

\subsection{Model Configurations}

\paragraph{Subtask 1 (DimASR):}
We utilize \texttt{roberta-large} \cite{liu2019robertarobustlyoptimizedbert} as the regression encoder. Training is performed using the AdamW optimizer \cite{loshchilov2019decoupledweightdecayregularization} with an initial learning rate of $1.5e^{-5}$ and a batch size of 128. To preserve the pre-trained representations in the lower layers of the encoder, we apply layer-wise learning rate decay with a decay factor of 0.9, alongside a standard weight decay of 0.01. Models are trained for up to 20 epochs, with early stopping enforced using a patience of 3 epochs based on development set CCC validation \cite{7472669}. The final objective is a weighted combination of KL divergence (LDL) \cite{geng2016labeldistributionlearning} and CCC loss. To ensure robustness against initialization variance, we apply multi-seed ensembling across three random seeds (100, 42, 2026).

\paragraph{Subtask 2 (DimASTE):}
Triplet extraction is implemented by fine-tuning a \texttt{Qwen2.5-7B-Instruct} \cite{qwen2025qwen25technicalreport} LLM using QLoRA \cite{hu2021loralowrankadaptationlarge}. We set rank $r=32$, alpha $\alpha=64$, and optimize using a cosine learning rate scheduler with an initial rate of $1.5e^{-5}$. Early stopping patience is set to 5 validation steps. For dynamic prompt augmentation at inference (RAG-ICL) \citep{lewis2021retrievalaugmentedgenerationknowledgeintensivenlp, dong-etal-2024-survey}, we retrieve the top $k=3$ semantically similar training examples using the \texttt{BAAI/bge-base-en-v1.5} \cite{chen-etal-2024-m3} embedding model. 

\paragraph{Subtask 3 (DimASQP):}
For categorical prediction, we utilize \texttt{sup-simcse-roberta-large} as the contrastive encoder \cite{gao-etal-2021-simcse}. To mitigate the severe class imbalance observed in the aspect category taxonomy, training employs balanced $P-K$ sampling with $P=4$ classes and $K=4$ instances per batch. We optimize the Supervised Contrastive Loss \cite{khosla2021supervisedcontrastivelearning} using a temperature of $\tau=0.07$. Following training, class centroids are computed across the embedding space for nearest-centroid inference \cite{snell2017prototypicalnetworksfewshotlearning}.

\begin{comment}
\subsection{Implementation Details}
Hyperparameters were optimized based on development set performance across all subtasks. Our pipeline is implemented in PyTorch\footnote{Code and models are publicly available at \url{https://github.com/denislaschenko/HCC_dimABSA/tree/main/src}} utilizing the HuggingFace \texttt{Transformers} and \texttt{PEFT} libraries for LoRA adaptation, \texttt{BitsAndBytes} for 4-bit quantization, and \texttt{SentenceTransformers} for RAG retrieval. All models were trained and evaluated on an NVIDIA RTX 4000 Ada GPU utilizing mixed-precision (bf16) training.

\subsection{Evaluation Metrics}
We report performance using the official SemEval-2026 Task 3 CodaBench evaluation metrics. Subtask 1 is evaluated using RMSE, while subtask 2 and 3 use a continuous F1 score, as described in the dataset paper. \cite{lee2026dimabsabuildingmultilingualmultidomain}
\end{comment}

\paragraph{Implementation Details:} Hyperparameters were optimized based on development set performance. Our pipeline is implemented in PyTorch \cite{paszke2019pytorchimperativestylehighperformance} utilizing HuggingFace Transformers \cite{wolf-etal-2020-transformers}, PEFT (LoRA), BitsAndBytes (4-bit quantization), and SentenceTransformers \cite{reimers-gurevych-2019-sentence} (RAG retrieval). All models were trained on an NVIDIA RTX 4000 Ada GPU utilizing mixed-precision (bf16).\footnote{Code and models are publicly available at \url{https://github.com/denislaschenko/HCC_dimABSA/tree/main/src}}

\section{Results and Analysis}

We report performance using the official SemEval-2026 Task 3 \cite{yu-etal-2026-semeval} CodaBench metrics: RMSE for Subtask 1, and continuous F1 (cF1) for Subtasks 2 and 3 \cite{lee2026dimabsabuildingmultilingualmultidomain}. Tables 1 and 2 summarize the performance of our cascading pipeline.

Tables \ref{tab:results_st1} and \ref{tab:results_st23} summarizes the performance of our cascading pipeline across all three subtasks.

\begin{table}[h]
\centering
\small
\begin{tabular}{lccc}
\toprule
\textbf{Domain} & \textbf{PCC\_V} $\uparrow$ & \textbf{PCC\_A} $\uparrow$ & \textbf{RMSE\_VA} $\downarrow$ \\
\midrule
Laptop & 0.8531 & 0.5379 & 1.2942 \\
Restaurant & 0.8870 & 0.6462 & 1.3011 \\
\midrule
System Average & - & - & 1.4937 \\
\midrule
\textit{Average} & \textit{0.8700} & \textit{0.5920} & \textit{1.2976} \\
\bottomrule
\end{tabular}
\caption{DimASR (Subtask 1) regression performance. In comparison to the system paper using GPT-oss 120B Supervised Fine-Tuning \cite{lee2026dimabsabuildingmultilingualmultidomain} $\downarrow$ indicates lower is better; $\uparrow$ indicates higher is better.}
\label{tab:results_st1}
\end{table}

\begin{table}[h]
\centering
\small
\begin{tabular}{lcccc}
\toprule
\textbf{Task \& Domain} & \textbf{cPrecision} & \textbf{cRecall} & \textbf{cF1} \\
\midrule
\textbf{DimASTE (Subtask 2)} & & & \\
\quad Laptop & 0.6373 & 0.5050 & 0.5635 \\
\quad Restaurant & 0.6855 & 0.5873 & 0.6326 \\
\midrule
\quad \textit{System Average} & 0.5249 & 0.4737 & 0.4979 \\ 
\midrule
\quad \textit{Average} & \textit{0.6614} & \textit{0.5461} & \textit{0.5980} \\
\midrule
\textbf{DimASQP (Subtask 3)} & & & \\
\quad Laptop & 0.2867 & 0.2235 & 0.2512 \\
\quad Restaurant & 0.6082 & 0.5210 & 0.5612 \\
\midrule
\quad \textit{System Average} & 0.3896 & 0.3547 & 0.3712\\
\midrule
\quad \textit{Average} & \textit{0.4474} & \textit{0.3723} & \textit{0.4062} \\
\bottomrule
\end{tabular}
\caption{System performance on continuous Triplet (DimASTE) and Quadruplet (DimASQP) extraction. In comparison to the system paper using GPT-oss 120B Supervised Fine-Tuning \cite{lee2026dimabsabuildingmultilingualmultidomain}}
\label{tab:results_st23}
\end{table}

On DimASR (Subtask 1), our LDL-based regressor proved robust against subjective annotation variance, achieving an RMSE of 1.2942 (laptop) and 1.3011 (restaurant), significantly outperforming the massive GPT-OSS 120B Supervised Fine-Tuning (SFT) baseline, which achieved 1.5269 and 1.4605 respectively \cite{lee2026dimabsabuildingmultilingualmultidomain}. This confirms that explicit distribution modeling is vastly superior to LLM-based scalar generation. For DimASTE (Subtask 2), our modular Qwen-7B pipeline achieved an average cF1 of 0.5980. This demonstrates the viability of Retrieval-Augmented In-Context Learning (RAG-ICL) as a resource-efficient alternative to the GPT-OSS 120B baseline established by the task organizers on both the laptop (0.5635 vs. 0.4515) and restaurant (0.6326 vs. 0.5442) domains.\cite{lee2026dimabsabuildingmultilingualmultidomain}. For DimASQP (Subtask 3), the system achieved an average cF1 of 0.4062. While performance naturally dropped on the densely overlapping \texttt{laptop} domain (cF1 0.2512), our contrastive nearest-centroid approach still strictly outperformed the best-performing Llama-3.3 70B \cite{grattafiori2024llama3herdmodels} baseline (cF1 0.2483). Furthermore, on the orthogonal \texttt{restaurant} domain, our system achieved a cF1 of 0.5612, surpassing the 70B baseline (0.5048).
\subsection{Architectural Merits and Error Analysis}
Although our system does not claim state-of-the-art absolute performance across all metrics, the results validates several critical architectural advantages of our decoupled, modular approach over monolithic end-to-end generative models.
\paragraph{Efficiency and Modularity:} The official DimABSA baselines indicate that maximizing cF1 often requires fine-tuning models with 70B to 120B parameters. In contrast, our pipeline achieves competitive extraction capabilities using only a 7B-parameter generative model. By decoupling the extraction phase from the continuous sentiment scoring, we entirely bypass the issue of numerical hallucination in LLMs \cite{Huang_2025}. The regression is handled by a lightweight, mathematically bounded RoBERTa model, allowing for rapid iteration and deployment on consumer-grade hardware.

\begin{comment}
\paragraph{Domain Ontology Discrepancy:} The primary bottleneck in our pipeline occurred during Subtask 3 on the \texttt{laptop} domain. We attribute this degradation directly to the structural topology of the predefined aspect categories. The \texttt{restaurant} categories are largely orthogonal (e.g., distinguishing \texttt{FOOD} from \texttt{SERVICE}), allowing our Supervised Contrastive Learning (SupCon) encoder to cleanly separate them. Conversely, the \texttt{laptop} domain exhibits a dense, long-tailed semantic overlap among technical attributes (e.g., distinguishing \texttt{DISPLAY\#QUALITY} from \texttt{DISPLAY\#DESIGN\_FEATURES}). Because our SupCon module relies on nearest-centroid inference, this dense semantic overlap led to a high rate of False Negatives (1497) compared to the restaurant domain (940). 
\end{comment}

\paragraph{Impact of Fuzzy Grounding:} Our post-hoc fuzzy grounding proved crucial for bridging the gap between generative LLM outputs and strict extraction metrics. The relatively tight clustering of continuous precision (0.6614) and recall (0.5461) in DimASTE indicates that the heuristic successfully anchored the LLM's inherently flexible generated spans back to the exact textual boundaries of the input sequence.


\section{Conclusion}

We presented a decoupled pipeline for SemEval-2026 Task 3 DimABSA. Unlike monolithic LLMs, our modular architecture achieves competitive performance through task-specific optimizations. For DimASR, combining LDL \cite{geng2016labeldistributionlearning} with a CCC objective \cite{7472669} successfully mitigated annotation noise. For DimASTE/DimASQP, QLoRA \cite{hu2021loralowrankadaptationlarge} paired with RAG-ICL \citep{lewis2021retrievalaugmentedgenerationknowledgeintensivenlp,dong-etal-2024-survey} on a 7B model proved a resource-efficient alternative to 120B-parameter baselines. Finally, post-hoc fuzzy grounding bridged the gap between generative flexibility and strict metrics. This work highlights that neuro-symbolic modularity and targeted representations can rival brute-force scaling in complex NLP tasks.

\vspace{3mm}
\section*{Limitations}

While our modular pipeline provides a resource-efficient approach to Dimensional ABSA, it presents several inherent limitations. First, as a decoupled cascading system, it is susceptible to error propagation. If the generative Qwen-7B module fails to extract a valid aspect-opinion span during the initial phase, the downstream DimASR regressor cannot recover the missing instance, capping the maximum achievable recall. 

Second, our Supervised Contrastive Learning (SupCon) \cite{khosla2021supervisedcontrastivelearning} module combined with nearest-centroid inference struggles with dense, long-tailed ontologies. While highly effective for orthogonal domains like \texttt{restaurant}, the system exhibits significant performance degradation in domains with heavily overlapping technical attributes, such as the \texttt{laptop} domain. Future iterations would require hierarchical contrastive boundaries to resolve these fine-grained semantic overlaps. 

Finally, our system was developed and evaluated exclusively on the English (\texttt{eng}) track of the DimABSA dataset \cite{lee2026dimabsabuildingmultilingualmultidomain}. Because our fuzzy span grounding heuristic relies on token-level string matching, its efficacy on morphologically rich or zero-space languages (e.g., Chinese or Japanese) remains untested and would likely require language-specific tokenizer adaptations. We aim to address these limitations in our future work.

\section*{Acknowledgments}
The authors would like to thank the anonymous reviewers and task organizers. We thank the TUM and Prof. Jana Diesner and especially our mentor Shaghayegh Kolli. 
\bibliography{custom}

\appendix
\section{Formal Mathematical Definitions}
\label{sec:appendix_math}

While the main text provides a high-level overview of our architecture, we include the formal mathematical definitions of our core objective functions here to ensure full reproducibility and to justify our deviation from standard mean squared error (MSE) regression.

\subsection{Label Distribution and CCC Loss (DimASR)}
Standard continuous sentiment analysis often treats target variables as absolute scalars, optimizing via MSE. However, human affect—especially in subjective domains like restaurant reviews—exhibits intrinsic variance. Therefore, we model the gold scalar Valence-Arousal (VA) scores as a Gaussian distribution over $B=9$ discrete bins, effectively shifting the task from scalar regression to distribution learning. 

Let $\mathbf{p}$ be the gold label distribution and $\mathbf{\hat{p}}$ be the model's predicted softmax probability distribution over the bins. We optimize the Label Distribution Learning (LDL) loss using Kullback-Leibler (KL) divergence \cite{geng2016labeldistributionlearning}, which forces the model to capture the variance and uncertainty of the target rather than just its mean:
\begin{equation}
\mathcal{L}_{LDL} = \sum_{b=1}^{B} p_b \log \left( \frac{p_b}{\hat{p}_b} \right)
\end{equation}

While LDL captures local distributional shape, it does not inherently prevent global scaling shifts or penalize inverse correlations. To enforce structural trend alignment between predictions and ground truth, we combine LDL with the Concordance Correlation Coefficient (CCC) \cite{7472669}, a standard reliability metric in affective computing. Let $y$ and $\hat{y}$ denote the ground-truth and predicted scalars (derived via mathematical expectation over the bins), with means $\mu_y, \mu_{\hat{y}}$ and variances $\sigma_y^2, \sigma_{\hat{y}}^2$. The CCC is defined as:
\begin{equation}
\text{CCC} = \frac{2 \rho \sigma_y \sigma_{\hat{y}}}{\sigma_y^2 + \sigma_{\hat{y}}^2 + (\mu_y - \mu_{\hat{y}})^2}
\end{equation}
where $\rho$ is the Pearson correlation coefficient. By explicitly penalizing deviations from the 45-degree line of perfect concordance (via the squared difference of the means), the CCC objective directly mitigates the "regression to the mean" effect common in subjective NLP tasks.

\subsection{Supervised Contrastive Loss (DimASQP)}
For Subtask 3 (DimASQP), our system must categorize extracted aspects into predefined ontologies (e.g., \texttt{FOOD\#QUALITY}). We observed severe class imbalance and semantic overlap in the training data, rendering standard cross-entropy classifiers brittle. To address this, we implement Supervised Contrastive Learning (SupCon) \cite{khosla2021supervisedcontrastivelearning}. 

For a batch of $L_2$-normalized embeddings $\{\mathbf{z}_i\}$ with labels $\{y_i\}$, the SupCon loss pulls embeddings of the same semantic class together while forcefully pushing apart representations of different classes:
\begin{equation}
\resizebox{\linewidth}{!}{$\displaystyle 
\mathcal{L}_{SupCon} = \sum_i \frac{-1}{|P(i)|} \sum_{p \in P(i)} \log \frac{\exp(\mathbf{z}_i \cdot \mathbf{z}_p / \tau)}{\sum_{a \neq i} \exp(\mathbf{z}_i \cdot \mathbf{z}_a / \tau)}$}
\end{equation}
where $P(i)$ denotes the set of indices of all positive samples in the batch for instance $i$, and $\tau$ is a scalar temperature hyperparameter. This structurally enforces a margin of separation, allowing for highly robust nearest-centroid inference even for underrepresented classes.

\section{Evaluation Metrics}
\label{sec:appendix_metrics}

To facilitate direct comparison with future work, we strictly follow the official SemEval-2026 Task 3 guidelines \cite{yu-etal-2026-semeval} for all reported evaluations. We detail the continuous formulation of these metrics below.

\subsection{Root Mean Square Error (DimASR)}
Subtask 1 evaluates the pure regression capability of the pipeline using a joint Root Mean Square Error ($RMSE_{VA}$) across both affective dimensions. Where $N$ is the total number of instances, $V_p^{(i)}, A_p^{(i)}$ denote the predicted values, and $V_g^{(i)}, A_g^{(i)}$ denote the gold values:
\begin{equation}
\resizebox{\linewidth}{!}{$\displaystyle RMSE_{VA} = \sqrt{ \frac{1}{N} \sum_{i=1}^{N} \left( (V_p^{(i)} - V_g^{(i)})^2 + (A_p^{(i)} - A_g^{(i)})^2 \right) }$}
\end{equation}

\subsection{Continuous F1 (DimASTE \& DimASQP)}
Standard ABSA metrics rely on binary strict-matching (a triplet is either perfectly extracted or entirely wrong). Because DimABSA introduces continuous targets, Subtasks 2 and 3 utilize a continuous F1 (cF1) score. This metric softly penalizes numerical deviations while strictly enforcing exact categorical span matching. 

First, a normalized Euclidean distance is calculated between the predicted continuous tuple $VA_p$ and the gold tuple $VA_g$:
\begin{equation}
dist(VA_p, VA_g) = \frac{\sqrt{(V_p - V_g)^2 + (A_p - A_g)^2}}{D_{max}}
\end{equation}
where $D_{max} = \sqrt{8^2 + 8^2} = \sqrt{128}$ represents the maximum possible theoretical error distance in the bounded $[1, 9]$ Valence-Arousal coordinate space.

The standard True Positive is then relaxed into a continuous true positive ($cTP$) for a given extraction $t$:
\begin{equation}
cTP^{(t)} = 
\begin{cases} 
1 - dist(VA_p^{(t)}, VA_g^{(t)}), & t \in P_{cat} \\
0, & \text{otherwise}
\end{cases}
\end{equation}
where $P_{cat}$ denotes predictions where all textual and categorical elements (Aspect, Opinion, and optionally Category) match the gold annotation exactly. Continuous Precision (cPrec) and Recall (cRec) are calculated by dividing the summed $cTP$ by the total number of predicted and gold tuples, respectively, resulting in the final harmonic mean (cF1).

\section{Ethical Considerations}
\label{sec:appendix_ethics}
While Dimensional Aspect-Based Sentiment Analysis (DimABSA) offers fine-grained insights into user opinions, it is critical to recognize the epistemological limits of text-based emotion recognition. As noted by the shared task organizers, individuals express attitudes towards entities in highly complex and idiosyncratic ways \cite{yu-etal-2026-semeval}. The annotations used to train our models inherently capture \textit{perceived} sentiment as interpreted by human annotators, which may differ significantly from the true, internal psychological state of the original author.

Because language serves as a shared mechanism for communication, perceived opinions correlate reliably with actual opinions at an aggregate level (e.g., evaluating general product reception across thousands of reviews). However, severe ethical risks arise if this architecture is applied at the micro-level. Because our DimASR module outputs precise, real-valued numbers (e.g., Valence: 2.88), there is an inherent risk of "false objectivity"—downstream users or automated decision systems might misinterpret these numerical predictions as definitive psychological metrics. We explicitly warn against deploying this architecture in sensitive, individual-facing domains (such as employment screening, judicial sentencing, or mental health triage) where the gap between perceived text and true human emotion could lead to tangible harm.

\begin{figure*}[h]
\centering
\includegraphics[width=0.95\linewidth]{latex/graphics/histogram.png}
    \caption{Distribution of gold Valence and Arousal labels across the consolidated training set. The complex, bimodal nature of Valence provides direct empirical justification for our distribution-aware modeling approach over standard scalar regression.}
    \label{fig:histograms}
    \includegraphics[width=0.95\linewidth]{latex/graphics/residuals.png}
    \caption{Predicted vs. Gold labels for Valence and Arousal. The red dashed line indicates perfect CCC concordance.}
    \label{fig:residuals}
\end{figure*}

\section{Detailed Input and Output Formulations}
\label{sec:appendix_io}

Given the multi-stage, hierarchical nature of the DimABSA dataset, data formatting is a non-trivial component of our pipeline. We explicitly decouple the numerical prediction from the textual extraction. To clarify how data flows through our cascading modules, the following is a complete JSON input-output formulation for the "Thai food" restaurant review referenced in Section 1.

\vspace{0.2cm}
\noindent \textbf{Subtask 1: Dimensional Aspect Sentiment Regression (DimASR)}
\begin{footnotesize}
\begin{verbatim}
Input:
{
  "ID": "R001",
  "Text": "average to good thai food, 
   but terrible delivery.",
  "Aspect": ["thai food", "delivery"]
}
Output:
{
  "ID": "R001",
  "Aspect_VA": [
    {"Aspect": "thai food", "VA": "6.75#6.38"},
    {"Aspect": "delivery", "VA": "2.88#6.62"}
  ]
}
\end{verbatim}
\end{footnotesize}

\vspace{0.2cm}
\noindent \textbf{Subtask 2: Dimensional Aspect Sentiment Triplet Extraction}
\begin{footnotesize}
\begin{verbatim}
Input:
{
  "ID": "R001",
  "Text": "average to good thai food,
   but terrible delivery."
}
Output:
{
  "ID": "R001",
  "Triplet": [
    {
      "Aspect": "thai food", 
      "Opinion": "average to good", 
      "VA": "6.75#6.38"
    },
    {
      "Aspect": "delivery", 
      "Opinion": "terrible", 
      "VA": "2.88#6.62"
    }
  ]
}
\end{verbatim}
\end{footnotesize}

\vspace{0.2cm}
\noindent \textbf{Subtask 3: Dimensional Aspect Sentiment Quad Prediction}
\begin{footnotesize}
\begin{verbatim}
Input:
{
  "ID": "R001",
  "Text": "average to good thai food, but terrible 
   delivery."
}
Output:
{
  "ID": "R001",
  "Quadruplet": [
    {
      "Aspect": "thai food", 
      "Category": "FOOD#QUALITY", 
      "Opinion": "average to good", 
      "VA": "6.75#6.38"
    },
    {
      "Aspect": "delivery", 
      "Category": "SERVICE#GENERAL", 
      "Opinion": "terrible", 
      "VA": "2.88#6.62"
    }
  ]
}
\end{verbatim}
\end{footnotesize}

\begin{figure*}[h]
    \centering
\includegraphics[width=0.9\linewidth]{latex/graphics/poster_tsne_comparison.png}
    \caption{t-SNE visualization of the SupCon embedding space for the Restaurant domain. Distinct clustering of distinct categories enables highly robust, parameter-free nearest-centroid inference.}
    \label{fig:tsne}
\end{figure*}

\section{Instruction-Tuning Prompt Template}
\label{sec:appendix_prompt}

For Subtask 2 (DimASTE) \cite{yu-etal-2026-semeval}, we observed that base generative models frequently hallucinate boundaries or generate 'NULL' values when unsure. We designed a strict, rule-based prompt template to enforce the extraction of aspect-opinion pairs. Crucially, we offload numerical regression to the DimASR module, instructing the LLM only to format the output. 

During our Retrieval-Augmented In-Context Learning (RAG-ICL) \citep{lewis2021retrievalaugmentedgenerationknowledgeintensivenlp, dong-etal-2024-survey} inference phase, up to $k=3$ semantically similar training examples were prepended to this template to provide dynamic few-shot guidance. The base system prompt is structured as follows:

\vspace{0.2cm}
\begin{footnotesize}
\begin{verbatim}
"""Given a textual instance [Text],
extract all (A, O, VA) triplets, where:
- A is an Aspect term (a phrase
describing an entity mentioned in [Text])
- O is an Opinion term
- VA is a Valence–Arousal score in the
format (valence#arousal)

RULES:
1. EXTRACT EXACTLY: Copy the substring 
exactly as it appears in the text, 
including typos and capitalization.
2. NO NULLS: Every triplet must have
explicit Aspects and Opinions from the
text. Do not generate 'NULL'.
3. NO HALLUCINATIONS: If a word is not in 
the text, do not extract it.
4. VA FORMAT: Predict 0#0 for all aspects.

Be exhaustive: extract every aspect and 
opinion mentioned. You must preserve the
EXACT capitalization and spelling as it 
appears in the [Text]."""
\end{verbatim}
\end{footnotesize}

\section{Data Distribution and Regression Error Analysis}
\label{sec:appendix_regression_analysis}

A core claim of our methodology is that continuous subjective sentiment cannot be accurately modeled with simple scalar regression due to inherent annotation variance. Figure \ref{fig:histograms} empirically validates this claim. The distribution of gold Valence annotations is highly non-normal and heavily bimodal, representing polarizing opinions. Standard Mean Squared Error (MSE) models struggle severely with bimodal targets, often predicting the "dead center" between the peaks. Our Label Distribution Learning (LDL) approach \citep{geng2016labeldistributionlearning} successfully models these complex distribution shapes.

Furthermore, Figure \ref{fig:residuals} visualizes the residuals of our DimASR module, plotting predicted versus gold labels. The scatter plots offer a visual explanation for the performance discrepancies observed in our results table. While Valence predictions tightly hug the 45-degree perfect-concordance line, Arousal exhibits a noticeable "regression to the mean" effect at the extreme bounds. This visually confirms that predicting human emotional energy (Arousal) from text alone remains a significantly harder task than predicting polarity (Valence), necessitating the heavier loss weighting we applied to the Arousal dimension.

\section{Embedding Space Visualizations}
\label{sec:appendix_tsne}

Our decision to bypass standard classification heads in favor of Supervised Contrastive Learning (SupCon) \cite{khosla2021supervisedcontrastivelearning} for Subtask 3 (DimASQP) was driven by the need for semantic robust boundaries. Figure \ref{fig:tsne} visualizes the learned embedding space for the restaurant domain using t-SNE \cite{gao-etal-2021-simcse}. 

The visualization confirms our architectural hypothesis: the SupCon objective successfully forces orthogonal categories (such as \texttt{FOOD\#QUALITY} vs. \texttt{SERVICE\#GENERAL}) into distinct, tightly packed clusters. This clear margin of separation is what enables our computationally lightweight nearest-centroid matching inference to achieve high continuous F1 (cF1) performance, efficiently overcoming the task's inherent category imbalance.

\end{document}
