\pdfoutput=1
\documentclass[11pt]{article}

\usepackage{acl}

\usepackage{times}
\usepackage{latexsym}

\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{microtype}

\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{multirow}
\usepackage{listings}

\title{Scmhl5 at SemEval-2026 Task 3: Uncertainty-Aware Adversarial Learning for Embedding Enhancement in Dimensional Aspect-Based Sentiment Analysis}

\author{Haohuan Chen \and Han Liu \\
  College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China \\
  \texttt{chenhaohuan@szdx.wecom.work}, \texttt{han.liu@szu.edu.cn} \\}

\begin{document}
\maketitle
\begin{abstract}
This paper presents an uncertainty-aware adversarial learning framework developed for SemEval-2026 Task 3, a shared task focusing on Dimensional Aspect-Based Sentiment Analysis (ABSA). Our framework involves three key components: Uncertainty modeling, Heterogeneous Mixture-of-Experts (HMoE) architecture, and embedding-level adversarial training. Experimental results demonstrate that our framework effectively reduces the Root Mean Square Error (RMSE), thereby validating the synergistic advantages of uncertainty modeling and heterogeneous fusion strategies in fine-grained sentiment regression tasks.
\end{abstract}

\section{Introduction}
Dimensional ABSA is a fine-grained affective computing task that provides a more nuanced understanding of human emotions than traditional classification. It requires predicting continuous Valence and Arousal scores rather than discrete class labels. Valence represents the nature of an emotion, while Arousal represents its intensity. For instance, although "frustrated" and "depressed" both share a negative Valence, they differ significantly in their Arousal levels.

In this shared task, we address the challenge of undertaking the regression task across a diverse set of languages, ranging from widely spoken languages like English and Chinese to under-represented African languages such as Swahili and Nigerian Pidgin. 

The main challenges of this task lie in the representational difference between high-resource and low-resource languages, and the inherent noise in the human sentiment scoring process. Standard multilingual models may not sufficiently capture the unique features of African languages while performing well on languages like English or German. Furthermore, as human labels may be subjective and inconsistent, traditional methods that perform point estimation can lead to overfitting instead of learning the true underlying sentiment trends.

To address these challenges, we propose an uncertainty-aware adversarial framework that strategically combines heterogeneous encoders with probabilistic modeling. The main contributions of our work are as follows.

\paragraph{Language-Specific HMoE Framework} 
We propose a HMoE architecture that is trained independently for each target language. By creating training instances for separate expert models, we enable the gating mechanism to specialize in the unique linguistic characteristics of each domain. This allows the system to adaptively fuse representations from a general expert and a language-specific expert, maximizing the representational power on a per-language basis.

\paragraph{Uncertainty-Aware Adversarial Learning} 
We implement a robust regression strategy that synergizes Gaussian distribution modeling with adversarial training based on Fast Gradient Method (FGM). While the Gaussian Negative Log-Likelihood (NLL) loss enables the model to attenuate the impact of subjective annotation noise by quantifying aleatoric uncertainty, FGM enforces a smoother decision boundary in the embedding manifold. The synergy between uncertainty modeling and adversarial regularization effectively prevents overfitting to inconsistent human labels within each specific language track\footnote{Our code is publicly available at \url{https://github.com/haohuanchen/UUA}}.


\section{Background}
\subsection{Dataset and Preparation}
We participated in both Track A and Track B of the shared task, and our experiments were conducted only on the official datasets provided by the organizers \citep{yu-etal-2026-semeval}. These resources consist of the Track A dataset from \citet{lee2026dimabsabuildingmultilingualmultidomain} and the Track B dataset from \citet{becker2026dimstancemultilingualdatasetsdimensional}. Both tracks employ a consistent core schema comprising ID, text, aspects, and continuous Valence and Arousal scores. Each ID corresponds to a unique text and multiple aspects, with each aspect mapped to a single pair of VA scores. Consequently, we treat the “Aspect” as the primary input unit to ensure that the model produces a unique VA prediction for each instance.

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{figures/data.pdf} 
  \caption{Data preprocessing pipeline and aspect-level sample construction}
  \label{fig:data}
\end{figure}

As illustrated in Figure \ref{fig:data}, all VA scores are first normalized from their original scale to a range of [-1, 1] using a linear transformation to facilitate stable training. During the inference stage, the predicted values are rescaled to the original interval [1, 9] via a corresponding inverse mapping. Subsequently, we decompose text entries containing multiple aspects into distinct samples by concatenating the text with each individual aspect. This process ensures that each target aspect is treated as an independent input unit paired with its specific VA label. Our framework is primarily developed and iteratively refined on the Track B dataset, and was subsequently extended to generate the final predictions for Track A.

\subsection{Related Work}
Traditional approaches to ABSA have predominantly focused on categorical classification, assigning discrete polarity labels to specific targets \citep{pontiki-etal-2016-semeval}. However, such coarse-grained schemas often fail to encapsulate the nuance and intensity of human affect. Consequently, the field has increasingly adopted the dimensional emotion model rooted in the circumplex theory of affect \citep{russell1980circumplex}, which quantifies sentiment via continuous Valence and Arousal variables. Although earlier architectures based on LSTMs established foundational baselines for this regression task \citep{wang2016dimensional}, they struggle to capture long-range semantic dependencies in complex multilingual contexts. Recent advancements leveraging Transformer-based architectures have demonstrated superior capability in feature extraction \citep{mendes2023quantifying}, yet precise regression across typologically diverse languages remains challenging due to the inherent subjectivity of human annotation.

The paradigm of cross-lingual understanding has been revolutionized by massive multilingual pre-trained language models such as BERT \citep{devlin2019bert} and XLM-RoBERTa \citep{conneau2020unsupervised}. These models project diverse languages into a shared semantic space, enabling zero-shot transfer. Nevertheless, they frequently encounter the ``curse of multilinguality,'' where limited model capacity leads to performance degradation in low-resource languages when the vocabulary is dominated by high-resource data \citep{lauscher2020zero}. To mitigate this issue, language-specific adaptive pre-training has emerged as a vital strategy. \citet{alabi2022adapting} introduced Afro-XLMR, which was designed by adapting the XLMR architecture specifically for African languages by performing masked language modeling in a curated corpus of 17 languages. Conversely, for high-resource scenarios, DeBERTa \citep{he2020deberta} significantly improves natural language understanding through its disentangled attention mechanism. Our framework strategically fuses these heterogeneous experts to maximize representational power across differing linguistic domains.


\section{Framework Overview}

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{figures/system.pdf} 
  \caption{Overview of the uncertainty-aware adversarial learning framework.}
  \label{fig:system}
\end{figure}

Our proposed framework is an uncertainty-aware one built upon a HMoE architecture. As illustrated in Figure \ref{fig:system}, the framework addresses the linguistic disparity of the shared task by combining high-resource and low-resource encoders, while simultaneously modeling affective ambiguity through a probabilistic distribution.

\subsection{Heterogeneous Expert Modeling}
To optimize text representation across diverse languages, we treat the backbones as specialized experts. The input sequence is formatted as \texttt{[BOS] Sentence [SEP] Aspect [EOS]}, which is processed by two parallel streams: DeBERTa-v3-large serves as a high-resource expert to capture general semantic features, while Afro-XLMR-large acts as a language-specific expert to bridge the vocabulary gap in low-resource African languages. Each expert stream consists of a dedicated tokenizer, a transformer backbone and a dropout layer, ensuring that the model extracts complementary features from distinct linguistic semantic spaces.

\subsection{Uncertainty Modeling}
Consistent with the Gaussian distribution-based uncertainty modeling strategy, our framework does not treat sentiment as a deterministic point. Instead, each expert branch bifurcates into two MLP-based regression heads to estimate the parameters of a normal distribution $\mathcal{N}(\mu, \sigma^2)$. 

The mean head $\mu$ involves a Tanh activation function to bound the predicted sentiment intensity, and the uncertainty head $\sigma$ is provided with a Softplus activation to produce a positive standard deviation. This probabilistic formulation allows the model to explicitly represent the aleatoric uncertainty inherent in subjective human annotations.

\subsection{Learnable Gated Fusion}
The fusion of expert outputs is managed by a learnable gating network. This module takes the [CLS] tokens from both encoders as inputs, concatenates the inputs and passes the resulting vector through an MLP followed by a Softmax layer to generate expert weights $w_1$ and $w_2$. These weights dynamically determine the contributions of various experts based on the input context. The final parameters are calculated as: $\mu=w_1\mu_1+w_2\mu_2$ and $\sigma=w_1\sigma_1+w_2\sigma_2$. This uncertainty-aware fusion ensures that the predicted distribution is an adaptive mixture of the two experts' confidence levels. For final inference, we adopt this fused mean $\mu$ as the deterministic VA score.

\subsection{Embedding-Level Adversarial Training}
To enhance the robustness of HMoE, we incorporate embedding-level adversarial training using the FGM. As indicated by the dashed arrows in Figure~\ref{fig:system}, FGM calculates a norm-bounded perturbation based on the gradient of the Gaussian NLL Loss. This perturbation is injected directly into the word embedding layers of both encoders during training. By minimizing the loss on both clean and adversarial examples, the model is trained to maintain stable sentiment predictions even under minor linguistic perturbations, which is crucial for generalizing to noisy and low-resource datasets.


\section{Experimental setup}
\subsection{Training Implementation}
Our framework is implemented using PyTorch\footnote{\url{https://pytorch.org/}} and the Hugging Face Transformers library\footnote{\url{https://github.com/huggingface/transformers}}. We utilize \textbf{microsoft/deberta-v3-large} and \textbf{Davlan/afro-xlmr-large} as our core backbones. We adopt a per-language training strategy, where a dedicated model is developed independently for each dataset. This approach enables the gating mechanism to specifically optimize for the fine-grained linguistic nuances inherent in each language.

\subsection{Hyperparameter Settings}
The key hyperparameters used across all tracks are summarized as follows.
\paragraph{Learning Rate:} We employ the AdamW optimizer with a constant learning rate of $5 \times 10^{-6}$.
\paragraph{Batch Size:} The batch size is set to 16. For the more complex HMoE architectures, the batch size is adjusted based on memory constraints\footnote{All experiments were conducted on an NVIDIA GeForce RTX 4090 (24GB VRAM). Due to memory limitations, the batch size was set to 8 for HMoE and HMoE$_\text{U}$, and 4 for HMoE$_\text{A}$ and HMoE$_\text{UA}$.}.
\paragraph{Regularization:} Dropout of 0.2 and early stopping with a patience of 5 epochs based on validation RMSE.
\paragraph{Adversarial Settings:} FGM is applied to the word\_embeddings layer with a step size $\epsilon = 1.0$.
\paragraph{Reproducibility:} The random seed is fixed to 42. 

\subsection{Evaluation Metrics}
The primary evaluation metric for this shared task is the RMSE, calculated as:
\begin{equation}
\text{RMSE}_{VA} = \sqrt{{\sum_{i=1}^{N} \frac{\Delta V_i^2 + \Delta A_i^2}{N}}}
\end{equation}
where $N$ is the total number of instances, $\Delta V_i = V_p^{(i)} - V_g^{(i)}$ and $\Delta A_i = A_p^{(i)} - A_g^{(i)}$, $V_p^{(i)}$ and $A_p^{(i)}$ denote the predicted valence and arousal values for the $i$-th instance, and $V_g^{(i)}$ and $A_g^{(i)}$ denote the corresponding gold values.


\section{Results}
In this section, we present a comprehensive analysis of our experimental results. We first discuss the performance on Track B, which serves as our primary testbed for architectural optimization and ablation studies. We then evaluate the generalization of our framework on Track A.

\begin{table}[t]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{deu}$\downarrow$ & \textbf{eng}$\downarrow$ & \textbf{pcm}$\downarrow$ & \textbf{swa}$\downarrow$ & \textbf{zho}$\downarrow$ \\ 
\midrule
DeB              & 1.5696 & 1.7498 & 1.5104 & 2.3440 & 0.6873 \\
DeB$_\text{U}$   & 1.6782 & 1.8063 & 1.5903 & 2.4166 & 0.6765 \\
DeB$_\text{A}$   & 1.5407 & 1.7411 & 1.6500 & 2.4932 & 0.6844 \\
DeB$_\text{UA}$  & 1.5487 & 1.7181 & 1.4072 & 2.5741 & 0.6940 \\
\midrule
Afro             & 1.8272 & 2.0272 & 1.6717 & 2.0170 & 0.9318 \\
Afro$_\text{U}$  & 1.7066 & 1.9861 & 1.5921 & 2.6237 & 0.7146 \\
Afro$_\text{A}$  & 1.6242 & 2.0090 & 1.6053 & 1.9883 & 0.8730 \\
Afro$_\text{UA}$ & 1.5860 & 1.9558 & 1.6650 & \textbf{1.9391} & 0.7753 \\
\midrule
HMoE             & 1.5566 & 1.7877 & 1.5083 & 2.0069 & 0.6636 \\
HMoE$_\text{U}$  & 1.5166 & 1.7857 & 1.5051 & 2.2192 & 0.6757 \\
HMoE$_\text{A}$  & 1.4688 & 1.6872 & 1.5024 & 2.3040 & \textbf{0.6079} \\
HMoE$_\text{UA}$ & \textbf{1.4375} & \textbf{1.6612} & \textbf{1.3697} & 1.9426 & 0.6737 \\
\bottomrule
\end{tabular}
}
\caption{Main results on Track B. Subscripts U and A denote uncertainty modeling and adversarial training, respectively, and UA represents the combination of both U and A components.}
\label{tab:track_b}
\end{table}

\subsection{Performance on Track B}
The results for Track B across five diverse languages are summarized in Table \ref{tab:track_b}. Our iterative development focused on the interplay between the HMoE backbone, Gaussian NLL (U), and adversarial training (A).

\paragraph{Efficacy of HMoE}
The results show that HMoE generally performs better than using a single backbone. In Track B, HMoE$_\text{UA}$ achieves the lowest RMSE in most languages, including German (deu), English (eng), and Nigerian Pidgin (pcm). This phenomenon indicates that the gating mechanism can successfully combine the strengths of both encoders to improve overall accuracy. 

However, HMoE is not always the best choice. In Swahili (swa), the single model Afro$_\text{UA}$ performs slightly better than HMoE$_\text{UA}$. This suggests that for certain specific languages, the specialized encoder alone is already quite effective, and adding a second expert may not provide additional benefits or might even introduce slight interference.

\paragraph{Ablation of U and A Components}
The impacts of uncertainty modeling (U) and adversarial training (A) vary across languages and backbones.

Adversarial Training (A): FGM consistently provides a regularization benefit across most settings. In particular, HMoE$_\text{A}$ achieves the overall best RMSE in Chinese (zho). This phenomenon suggests that adversarial perturbations effectively help the model learn more robust features in the embedding space for this language.

Uncertainty Modeling (U): While U is designed to handle potential label noise, the effect of U can be double-edged. In Chinese (zho), adding U to HMoE$_\text{A}$ actually increases the RMSE. A possible reason is that the Chinese dataset possesses highly consistent and high-quality annotations. In such cases, the probabilistic softening of labels by the Gaussian loss may be unnecessary and could lead to slight underfitting. Conversely, in Nigerian Pidgin (pcm), where labels may be more subjective, the synergy of U and A remains highly effective.

\paragraph{Language-Specific Discussion}
Although Afro-XLMR is primarily designed for African languages, it contributes significantly to non-African tracks when fused. For instance, in German (deu) and Nigerian Pidgin (pcm), the HMoE variants consistently outperform the standalone DeB models. This validates the effectiveness of our choice of experts, the inclusion of Afro-XLMR provides representation diversity that captures linguistic features overlooked by the general semantic encoder.

In Nigerian Pidgin (pcm), while HMoE$_{U}$ and HMoE$_{A}$ lead to marginal improvements over the base model, their combination HMoE$_{UA}$ yields a substantial performance leap. This suggests that U and A target different aspects of the problem, where U mitigates label noise and A enhances embedding stability. Their interaction is thus important for languages with subjective labels.

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lcccccccccc}
\toprule
\textbf{Model} & 
\textbf{eng-lap}$\downarrow$ & \textbf{eng-res}$\downarrow$ & 
\textbf{jpn-fin}$\downarrow$ & \textbf{jpn-hot}$\downarrow$ & 
\textbf{rus-res}$\downarrow$ & \textbf{tat-res}$\downarrow$ & 
\textbf{ukr-res}$\downarrow$ & \textbf{zho-fin}$\downarrow$ & 
\textbf{zho-lap}$\downarrow$ & \textbf{zho-res}$\downarrow$ \\ 
\midrule
DeB$_\text{UA}$  & \textbf{1.3696} & 1.3168 & \textbf{0.9344} & \textbf{0.6811} & 1.4609 & 2.3184 & 1.5928 & 0.5557 & 0.7494 & \textbf{0.9838} \\
Afro$_\text{UA}$ & 1.4965 & 1.4353 & 0.9495 & 0.8454 & 1.5170 & 2.0689 & 1.5815 & \textbf{0.5158} & 0.7165 & 1.3707 \\
HMoE$_\text{UA}$ & 1.4275 & \textbf{1.2941} & 0.9538 & 0.7324 & \textbf{1.4172} & \textbf{2.0043} & \textbf{1.5196} & 0.5458 & \textbf{0.6821} & 0.9849 \\
\bottomrule
\end{tabular}
}
\caption{Main results on Track A.}
\label{tab:track_a}
\end{table*}

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lllcccccccc}
\toprule
\textbf{Subtask} & \textbf{Source} & \textbf{Model} & 
\textbf{eng-lap}$\uparrow$ & \textbf{eng-res}$\uparrow$ & 
\textbf{jpn-hot}$\uparrow$ & \textbf{rus-res}$\uparrow$ & 
\textbf{tat-res}$\uparrow$ & \textbf{ukr-res}$\uparrow$ & 
\textbf{zho-lap}$\uparrow$ & \textbf{zho-res}$\uparrow$ \\ 
\midrule
\multirow{8}{*}{Subtask 2}
& \multirow{4}{*}{GPT-4.1}
&   DeB$_\text{UA}$  & 0.5132 & 0.6127 & 0.3357 & 0.3960 & 0.3609 & 0.4206 & 0.3090 & 0.3955 \\
& & Afro$_\text{UA}$ & 0.5133 & 0.6036 & 0.3315 & 0.3951 & 0.3661 & 0.4192 & 0.3111 & 0.3858 \\
& & HMoE$_\text{UA}$ & 0.5136 & 0.6150 & 0.3363 & 0.3986 & 0.3649 & 0.4236 & 0.3096 & 0.3948 \\
\cmidrule{3-11}
& & Mean & \textbf{0.5134} & \textbf{0.6104} & \textbf{0.3345} & \textbf{0.3966} & \textbf{0.3640} & \textbf{0.4211} & \textbf{0.3099} & \textbf{0.3920} \\
\cmidrule{2-11}
& \multirow{4}{*}{Gold}
&   DeB$_\text{UA}$  & 0.9051 & 0.9101 & 0.9407 & 0.8937 & 0.8473 & 0.8841 & 0.9434 & 0.9303 \\
& & Afro$_\text{UA}$ & 0.9055 & 0.8983 & 0.9307 & 0.8888 & 0.8584 & 0.8798 & 0.9474 & 0.9098 \\
& & HMoE$_\text{UA}$ & 0.9044 & 0.9150 & 0.9405 & 0.8957 & 0.8544 & 0.8957 & 0.9478 & 0.9301 \\
\cmidrule{3-11}
& & Mean & \textbf{0.9050} & \textbf{0.9078} & \textbf{0.9373} & \textbf{0.8927} & \textbf{0.8534} & \textbf{0.8865} & \textbf{0.9462} & \textbf{0.9234} \\
\midrule
\multirow{8}{*}{Subtask 3}
& \multirow{4}{*}{GPT-4.1}
&   DeB$_\text{UA}$  & 0.2750 & 0.5119 & 0.2195 & 0.3138 & 0.2594 & 0.3338 & 0.1986 & 0.3309 \\
& & Afro$_\text{UA}$ & 0.2754 & 0.5040 & 0.2168 & 0.3127 & 0.2628 & 0.3326 & 0.1996 & 0.3226 \\
& & HMoE$_\text{UA}$ & 0.2752 & 0.5134 & 0.2199 & 0.3159 & 0.2629 & 0.3358 & 0.1990 & 0.3302 \\
\cmidrule{3-11}
& & Mean & \textbf{0.2752} & \textbf{0.5098} & \textbf{0.2187} & \textbf{0.3141} & \textbf{0.2617} & \textbf{0.3341} & \textbf{0.1991} & \textbf{0.3279} \\
\cmidrule{2-11}
& \multirow{4}{*}{Gold}
&   DeB$_\text{UA}$  & 0.9051 & 0.9101 & 0.9498 & 0.8937 & 0.8473 & 0.8841 & 0.9434 & 0.9303 \\
& & Afro$_\text{UA}$ & 0.9056 & 0.8983 & 0.9396 & 0.8888 & 0.8584 & 0.8798 & 0.9474 & 0.9098 \\
& & HMoE$_\text{UA}$ & 0.9044 & 0.9150 & 0.9496 & 0.8957 & 0.8544 & 0.8957 & 0.9478 & 0.9301 \\
\cmidrule{3-11}
& & Mean & \textbf{0.9050} & \textbf{0.9078} & \textbf{0.9463} & \textbf{0.8927} & \textbf{0.8534} & \textbf{0.8865} & \textbf{0.9462} & \textbf{0.9234} \\
\bottomrule
\end{tabular}
}
\caption{Results on Track A Subtasks 2 and 3. Bold values indicate the mean of each group.}
\label{tab:subtask_2_3}
\end{table*}

\subsection{Generalization on Track A}
As UA models outperform others on Track B, we further evaluate their performance on Track A, with results shown in Table \ref{tab:track_a}.

\paragraph{Strengths of HMoE} 
HMoE$_\text{UA}$ demonstrates the most balanced performance, achieving the lowest RMSE in 5 out of 10 subtasks. The model performs particularly well in Russian (rus), Tatar (tat), and Ukrainian (ukr). This performance suggests that for languages with complex morphology or limited resources, the gated fusion mechanism effectively combines the general semantic knowledge of DeBERTa with the multilingual strengths of Afro, leading to more stable predictions.

\paragraph{Expert Advantages in Specific Domains}
We also observe that the single-backbone models maintain strong advantages in certain areas. DeB$_\text{UA}$ remains highly competitive in high-resource languages such as English and Japanese, achieving the best scores in eng-lap and both Japanese subtasks. On the other hand, Afro$_\text{UA}$ shows unique potential in specific contexts, such as the Chinese financial domain (zho-fin). These variations indicate that while our HMoE framework is robust on average, the choice of the best expert is still influenced by the specific language and domain of the text.

\subsection{Evaluation on Subtasks 2 and 3}
Unlike Subtask 1, which focuses solely on VA regression, Subtask 2 and 3 require the extraction of additional sentiment elements. In Subtask 2, it is necessary to identify the “Aspect” and “Opinion” terms first, followed by the prediction of VA scores. Subtask 3 further adds the requirement for “Category” identification. Since our primary framework is optimized for VA regression, we implemented a two-stage pipeline to participate in these subtasks.

\paragraph{Pipeline with GPT-4.1}
In the first stage, we design prompts to guide the GPT-4.1 model to extract the required entities. In the second stage, the models previously trained on Subtask 1 are used to predict the VA scores. The results for this pipeline are summarized in the "GPT-4.1" section of Table~\ref{tab:subtask_2_3}. The nearly identical scores across models suggest that the performance bottleneck lies in LLM extraction rather than VA regression precision.

\paragraph{Verification via Gold Extraction}
We conduct an additional experiment for verification by using the gold labels of Aspects, Opinions, and Categories to perform VA prediction. As shown in the "Gold" rows of Table \ref{tab:subtask_2_3}, the scores get better drastically, reaching nearly identical high-performance levels regardless of the underlying model. This phenomenon confirms that the accuracy of element extraction is far more critical than the precision of VA scores. Although the performance of GPT-4.1 under the utilized few-shot prompts was relatively limited, we provide these prompts in Appendix \ref{sec:appendix_prompt} for future reference and comparison.

\section{Conclusion}
This paper presents an uncertainty-aware adversarial learning framework developed by our team for the SemEval-2026 Task 3 Dimensional ABSA task. By integrating a HMoE architecture with Gaussian distribution-based uncertainty modeling and FGM-based adversarial training, our framework effectively addresses the challenges of linguistic disparity and label subjectivity. Experimental results demonstrate that the proposed framework consistently reduces the RMSE, validating the synergistic advantages of heterogeneous fusion and uncertainty modeling in fine-grained affective computing tasks.

\section*{Acknowledgement}
This paper is supported in part by Natural Science Foundation of Shandong Province (Grant ZR2025QC1591) and Shenzhen Science and Technology Program (Grant ZDSYS20220527171400002).

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lcccccccccc}
\toprule
& \textbf{eng-lap} & \textbf{eng-res} & \textbf{jpn-fin} & \textbf{jpn-hot} & \textbf{rus-res} & \textbf{tat-res} & \textbf{ukr-res} & \textbf{zho-fin} & \textbf{zho-lap} & \textbf{zho-res}  \\ 
\midrule
Leaderboard Score$\downarrow$  &  N/A   & 1.3168 & 0.9292 & 0.6811 & 1.4609 & 2.0142 & 1.4732 &  N/A   & 0.7165 & 0.9838 \\
Leaderboard Rank               &  N/A   &  20th  &  10th  &  10th  &  9th   &  12th  &  8th   &  N/A   &   11th  &  14th  \\
\midrule
Final Score$\downarrow$        & 1.3696 & 1.2941 & 0.9344 & 0.6811 & 1.4172 & 2.0043 & 1.5196 & 0.5158 & 0.6821 & 0.9838 \\
Score$\Delta$                  &  ---   & 0.0227 & 0.0052 &  ---   & 0.0437 & 0.0099 & 0.0464 &  ---   & 0.0344 &  ---   \\              
\bottomrule
\end{tabular}
}
\caption{Leaderboard results for Track A Subtask 1. Due to technical anomalies during the submission phase, our predictions for \textbf{eng-lap} and \textbf{zho-fin} were not successfully recorded on the leaderboard.}
\label{tab:track_a_leaderboard}
\end{table*}

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{llcccccccc}
\toprule
& & \textbf{eng-lap} & \textbf{eng-res} & \textbf{jpn-hot} & \textbf{rus-res} & \textbf{tat-res} & \textbf{ukr-res} & \textbf{zho-lap} & \textbf{zho-res}  \\
\midrule
\multirow{4}{*}{Subtask 2}
& Leaderboard Score$\uparrow$ & 0.5136 & 0.6127 & 0.3357 & 0.3960 & 0.3649 & 0.4267 & 0.3111 & 0.3955  \\
& Leaderboard Rank            &  14th  &  13th  &  13th  &  15th  &  12th  &  13th  &  12th  &  12th   \\
\cmidrule{2-10}
& Final Score$\uparrow$       & 0.5134 & 0.6104 & 0.3345 & 0.3966 & 0.3640 & 0.4211 & 0.3099 & 0.3920  \\
& Score$\Delta$               & 0.0002 & 0.0023 & 0.0012 & 0.0006 & 0.0009 & 0.0056 & 0.0012 & 0.0035  \\
\midrule
\multirow{4}{*}{Subtask 3}
& Leaderboard Score$\uparrow$ & 0.2752 & 0.5119 & 0.2195 & 0.3138 & 0.2629 & 0.3384 & 0.1996 & 0.3309  \\
& Leaderboard Rank            &  12th  &  16th  &  9th   &  10th  &  9th   &  9th   &  11th  &  11th   \\
\cmidrule{2-10}
& Final Score$\uparrow$       & 0.2752 & 0.5098 & 0.2187 & 0.3141 & 0.2617 & 0.3341 & 0.1991 & 0.3279  \\
& Score$\Delta$               &  ---   & 0.0021 & 0.0008 & 0.0003 & 0.0012 & 0.0043 & 0.0005 & 0.0030  \\
\bottomrule
\end{tabular}
}
\caption{Leaderboard results for Track A Subtasks 2 and 3.}
\label{tab:subtask_2_3_leaderboard}
\end{table*}

\begin{table}[t]
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}{lccccc}
\toprule
& \textbf{deu} & \textbf{eng} & \textbf{pcm} & \textbf{swa} & \textbf{zho} \\ 
\midrule
Leaderboard Score$\downarrow$   & 1.4375 & 1.6612 & 1.4072 & 1.9391 & 0.6765 \\
Leaderboard Rank                &  4th   &  8th   &  5th   &  3rd   &  8th   \\
\midrule
Final Score$\downarrow$         & 1.4375 & 1.6612 & 1.3697 & 1.9391 & 0.6079 \\
Score$\Delta$                   &  ---   &  ---   & 0.0375 &  ---   & 0.0686 \\
\bottomrule
\end{tabular}
}
\caption{Leaderboard results for Track B.}
\label{tab:track_b_leaderboard}
\end{table}

\bibliography{custom}

\appendix

\section{Leaderboard Results}
To keep the standard and repeatability of the code, we cleaned up the code after the task. Because of setting updates and parameter changes, some results changed slightly. All results shown in this paper are from the final cleaned code. In this section, we provide a comprehensive comparison of our leaderboard scores, rankings, and final results for Track A Subtask 1 in Table \ref{tab:track_a_leaderboard}, Track A Subtasks 2 and 3 in Table \ref{tab:subtask_2_3_leaderboard}, and Track B in Table \ref{tab:track_b_leaderboard}, respectively.

\section{Prompt template}
\label{sec:appendix_prompt}
For Subtasks 2 and 3, we design a few-shot prompting strategy for GPT-4.1 to automatically extract aspects, opinions and categories. The prompt template is as follows.

\begin{lstlisting}[
    basicstyle=\small,
    breaklines=true,
    frame=single,
    columns=flexible
]
You are a strict aspect-opinion extractor.
Task: Extract all aspect-opinion pairs from the sentence and assign an entity and attribute.

Instructions:
- Extract all aspects and their corresponding opinions from the sentence.
- Output format must strictly be: aspect, opinion, entity, attribute
- Multiple pairs separated with ` | `
- If an aspect has no opinion, output `NULL` as the opinion
- If no aspect exists, output `NULL, NULL, entity, attribute`.
- Aspect and opinion must be exact words from the sentence.
- Do NOT add explanations or extra text.

Entity must be one of: {entity_list}
Attribute must be one of: {attribute_list}
- Entity and Attribute must be chosen strictly from the lists above.
- Do NOT invent new entity or attribute names.

Examples:
Sentence: {sentence sample1}
Output: {output sample1}

Sentence: {sentence sample2}
Output: {output sample2}

Sentence: {sentence sample3}
Output: {output sample3}

Now extract from the following sentence:
Sentence: {sentence}
Output:
\end{lstlisting}

\end{document}
