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\title{Team VYN at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis}

\author{
  Vishal Thenuwara, Yomal de Mel, \and Nisansa de Silva \\
  Department of Computer Science \& Engineering \\
  University of Moratuwa,   Sri Lanka \\
  \texttt{\{vishal.23,yomal.23,nisansa\}@cse.mrt.ac.lk}
}

\date{}

\begin{document}
\maketitle

\begin{abstract}
This paper describes our system for the DimABSA 2026 Shared Task (SemEval-2026 Task~3), which requires predicting continuous Valence-Arousal (VA) scores in $[1,9]$ for aspect-level sentiments instead of categorical labels. We develop an approach to address all three Track~A subtasks: aspect-level VA regression, triplet extraction. The DESS (DeBERTa Enhanced Syntactic-Semantic) model with VA Regression Head achieves 8.22\% micro cF1 on the development set on the triplet extraction subtask. We analyze the challenges of adapting categorical ABSA (Aspect Based Sentiment Analysis) architectures to continuous dimensional sentiment and discuss directions for improvement.
We release our code at \url{https://github.com/VishalRepos/DimABSA2026}.
\end{abstract}

\section{Introduction}

Aspect-Based Sentiment Analysis (ABSA) has traditionally relied on categorical sentiment labels (positive, negative, neutral) to characterize opinions toward specific aspects in text in domains such as customer reviews~\cite{jayakody-etal-2024-enhanced,pontiki-etal-2014-semeval,gunathilaka2022aspect,jayakody2024aspect,jayakody2024instruct} and law~\cite{mudalige2020sigmalaw,rajapaksha2020rule,rajapaksha2021sigmalaw,rajapaksha2021sigmalawJ}.
%
However, this coarse-grained representation fails to capture the nuanced intensity and emotional dimensions of human sentiment~\cite{de2025geesanbhava}. The DimABSA 2026 Shared Task \cite{lee2026dimabsa} addresses this limitation by introducing \textit{dimensional} sentiment analysis into the ABSA framework, where sentiment is represented as continuous Valence-Arousal (VA) scores.
%
We participate in Track~A (English), addressing all three subtasks across the restaurant and laptop review domains.

Our approach adapts the DESS architecture \cite{thenuwara2025dess}, a state-of-the-art span-based model for ASTE (Aspect Sentiment Triplet Extraction) that leverages dual-channel graph convolutional networks (GCNs) to capture both syntactic and semantic relationships. We modify the sentiment classification head to output continuous VA predictions. MSE is used in the DESS model and used for VA regression.

Our experiments reveal that directly adapting categorical span-extraction models to continuous regression is non-trivial.
The system achieves a micro F1 of 8.22\% for triplet extraction on the development set, with entity extraction proving more robust than VA prediction. %We release our code at \url{https://github.com/VishalRepos/dimabsa-2026}.

\section{Background}

\subsection{Related Work}

Aspect Sentiment Triplet Extraction has been addressed through span-based methods~\cite{xu-etal-2021-learning}, sequence tagging~\cite{wu-etal-2020-grid}, instruction learning~\cite{scaria-etal-2024-instructabsa,jayakody2024instruct} and generative approaches \cite{zhang-etal-2021-aspect-sentiment}. The DESS model~\cite{thenuwara2025dess} represents the model ASTE, using dual-channel GCNs to integrate syntactic dependency and semantic similarity information.
%
The key challenge lies in jointly performing span extraction (a structured prediction task) and VA regression (a continuous prediction task) within a unified framework.

\section{System Overview}

\subsection{Architecture}

We have suggested three different architectures for three subtasks in Figure~\ref{fig:subtask1_arch}, Figure~\ref{fig:subtask2_arch} and Figure~\ref{fig:subtask3_arch} respectively. Common updates applied on the DESS architecture are described in Appendix~\ref{app:dess_adaptation}, while further information and larger scale diagrams for the specific subtasks can be found in Appendix~\ref{app:arch_summaries}.

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{ReseachPapers/Images/FigSubtask1.png}
\caption{Architecture of the Subtask~1 (DimASR) regression model. The input text--aspect pair is encoded as \texttt{[CLS] aspect [SEP] text [SEP]} and passed through a DeBERTa-v3-base encoder. The \texttt{[CLS]} token representation is fed into a two-layer MLP regression head (768$\to$256$\to$2) with GELU activation and dropout, producing continuous Valence and Arousal predictions in $[1,9]$.}
\label{fig:subtask1_arch}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{ReseachPapers/Images/FigSubtask2.png}
\caption{Architecture of the Subtask~2 (DimASTE) Pipeline-DeBERTa system. A shared DeBERTa-v3-base encoder feeds seven task-specific heads (A, O, AO, OA, C, Valence, Arousal). Bidirectional Machine Reading Comprehension (MRC) extraction identifies aspect--opinion pairs through forward (A$\to$AO) and backward (O$\to$OA) passes, followed by cross-validation with confidence threshold $\beta{=}0.90$ to filter spurious pairings. Validated pairs are scored by the Valence and Arousal regression heads to produce (Aspect, Opinion, VA) triplets.}
\label{fig:subtask2_arch}
\end{figure}

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{ReseachPapers/Images/FigSubtask3.png}
\caption{Architecture of the Subtask~3 (DimASQP) quad prediction system. The model extends the Subtask~2 Pipeline-DeBERTa architecture by activating the Category head (C), which classifies each validated aspect--opinion pair into a domain-specific taxonomy (e.g., \texttt{FOOD\#QUALITY}, \texttt{SERVICE\#GENERAL}) via an MRC query. The training loss augments the Subtask~2 objective with a weighted category cross-entropy term $\beta \cdot \mathcal{L}_{\text{cat}}$, producing (Aspect, Category, Opinion, VA) quadruplets.}
\label{fig:subtask3_arch}
\end{figure}



\paragraph{Encoder.}
We use DeBERTa-v3-base \cite{he2021debertav3} as the pretrained encoder, producing 768-dimensional contextualised token representations.

\paragraph{BiLSTM Layer.}
A 2-layer bidirectional LSTM with hidden dimension 384 processes the encoder outputs, capturing sequential dependencies.The BiLSTM output is projected back to 768 dimensions via a linear layer.

\paragraph{Dual-Channel GCN.}
Two parallel GCN channels process the encoded representations:
Syntactic GCN (Syn-GCN): A 2-layer GCN operating on the dependency parse tree adjacency matrix, capturing syntactic relationships between tokens.Semantic GCN (Sem-GCN): A 2-layer GCN with multi-head self-attention that learns semantic similarity-based adjacency, capturing implicit semantic relationships.
%\end{itemize}

\paragraph{Tree Interaction Network (TIN).}
The TIN module fuses the dual-channel GCN outputs through residual connections and a BiLSTM fusion layer:

\begin{equation}
h_{\text{fuse}} = \text{MLP}(\text{BiLSTM}([h_{\text{syn}}; h_{\text{sem}}]))
\end{equation}

where $h_{\text{syn}}$ and $h_{\text{sem}}$ are the syntactic and semantic GCN outputs after residual connections with the original encoder features.

\paragraph{Entity Classification.}
Candidate entity spans (up to length 8) are classified into \texttt{\{None, Target, Opinion\}} using span representations formed by max-pooling token embeddings within each span, concatenated with a learned size embedding:
\begin{equation}
\hat{y}_{\text{ent}} = W_e [h_{\text{span}}; e_{\text{size}}] + b_e
\end{equation}

\paragraph{VA Regression Head.}
This is our key modification.
%The original D2E2S uses a linear classifier over sentiment types.
%We replace it with a regression head that outputs 2 continuous values (Valence, Arousal):
%\begin{equation}
%[\hat{V}, \hat{A}] = W_s [h_{\text{head}}; h_{\text{tail}}; h_{\text{ctx}}; e_{\text{size}_h}; e_{\text{size}_t}] + b_s
%\end{equation}
%where $h_{\text{head}}$ and $h_{\text{tail}}$ are the span representations of the aspect and opinion entities, $h_{\text{ctx}}$ is the context representation (CLS token), and $e_{\text{size}}$ are size embeddings.
%The output dimension changes from $|\mathcal{S}|$ (number of sentiment classes) to 2.% 
we predict Valence and Arousal scores for each aspect–opinion pair within a sentence. The model integrates the sentence-level embedding with contextual embeddings of the corresponding aspect and opinion terms. A base emotional representation derived from the sentence is refined through aspect-specific adjustments to capture localized sentiment intensity. The final Valence–Arousal scores are generated per aspect, enabling fine-grained emotion modeling within multi-aspect sentences.

\subsection{Training Objective}

The total loss combines entity classification loss and VA regression loss:
\begin{equation}
\mathcal{L} = \mathcal{L}_{\text{entity}} + \mathcal{L}_{\text{VA}} + 10 \cdot \mathcal{L}_{\text{batch}}
\end{equation}

The entity loss uses cross-entropy:
\begin{equation}
\mathcal{L}_{\text{entity}} = \frac{\sum_i m_i \cdot \text{CE}(\hat{y}_i, y_i)}{\sum_i m_i}
\end{equation}

where $m_i$ is the sample mask. % We further discuss the loss in Appendix~\ref{app:loss}.

The batch regularization loss $\mathcal{L}_{\text{batch}}$ in Eq.~\ref{eq:batch} penalizes high variance in VA predictions within each training batch, encouraging stable and consistent predictions:
\begin{equation}
\mathcal{L}_{\text{batch}} = \text{Var}(\hat{V}_{\text{batch}}) + \text{Var}(\hat{A}_{\text{batch}})
\label{eq:batch}
\end{equation}

The weight factor of 10 was determined through preliminary experiments on the development set; without this scaling, the batch regularization term had negligible effect on training dynamics due to its small magnitude relative to the entity and VA losses.



%The VA regression loss uses MSE, replacing the original cross-entropy sentiment loss:
%\begin{equation}
%\mathcal{L}_{\text{VA}} = \frac{\sum_j m_j \cdot \frac{1}{2}[(\hat{V}_j - V_j)^2 + (\hat{A}_j - A_j)^2]}{\sum_j m_j}
%\end{equation}


\section{Experimental Setup}

%\subsection{Data}
We use the DimABSA 2026 English dataset for Track~A, Subtask~2. Table~\ref{tab:data_stats} summarizes the dataset statistics. 
%
Further information on data is available in Appendix~\ref{app:data}. 
%
The subsequent preprocessing is described in Appendix~\ref{app:pre}. 
%
Hyperparameters used in the set up is discussed in Appendix~\ref{app:hyper}. 

\begin{table}[t]
\centering
\small
\begin{tabular}{llrr}
\toprule
\textbf{Domain} & \textbf{Statistic} & \textbf{Train} & \textbf{Test} \\
\midrule
\multirow{3}{*}{Restaurant}
  & Samples              & 1,448 & 200 \\
  & Avg.\ entities/sample  & 3.35  & --  \\
  & Avg.\ sentiments/sample & 1.68  & --  \\
\midrule
\multirow{3}{*}{Laptop}
  & Samples              & 2,279 & 200 \\
  & Avg.\ entities/sample  & 2.87  & --  \\
  & Avg.\ sentiments/sample & 1.43  & --  \\
\midrule
\multicolumn{2}{l}{\textbf{Total samples}} & 3,727 & 400 \\
\bottomrule
\end{tabular}
\caption{Dataset statistics for the English training and test sets, reported per domain.}
\label{tab:data_stats}
\end{table}



\section{Results}

\subsection{Main Results}

The DESS model shows a clear learning trajectory across training epochs.
In early epochs (1--2), the model produces no valid triplet predictions as both entity extraction and VA regression are still initializing.
By epoch~3, the model begins generating valid predictions with a micro F1 of 3.55\% (precision 5.18\%, recall 2.70\%).
Performance improves steadily, reaching 5.72\% F1 at epoch~4 and peaking at 8.22\% micro F1 at epoch~5 (precision 8.40\%, recall 8.06\%).
We note that the limited computational budget (Kaggle T4 GPUs, batch size of 4) constrained our ability to perform extensive hyperparameter tuning and longer training, which likely contributed to the modest performance.

\subsection{Subtask~1: DimASR}

The Subtask~1 model generates Valence--Arousal (VA) predictions for each text--aspect pair.
Table~\ref{tab:subtask1_examples} presents representative predictions from the development set across both laptop and restaurant domains.
Overall, the model demonstrates reasonable performance, with several predictions closely aligning with the ground truth, as reflected by low Euclidean distances (e.g., \textit{service} and \textit{staff}).
However, performance varies across aspects and domains.
While the model captures general sentiment trends for positively expressed aspects (e.g., \textit{food}, \textit{touchscreen}), it exhibits larger deviations for certain cases such as \textit{battery life} and \textit{sound}, indicating difficulty in accurately modeling nuanced or context-dependent emotional intensity.
These results suggest that although the model is capable of learning coarse-grained VA patterns, it struggles with fine-grained emotion estimation in more complex scenarios.



\begin{table}[t]
\centering
\small
\begin{tabular}{lccc}
\toprule
\multirow{2}{*}{\textbf{Aspect}} &  \multicolumn{2}{c}{\textbf{V\#A}} & \textbf{Euclidean } \\
\hhline{~--~}
 & \textbf{Actual} & \textbf{Predicted} & \textbf{Distance } \\
\midrule
\multicolumn{4}{l}{\textit{Laptop domain}} \\
touchscreen & 7.80\#7.60 & 7.00\#7.75 & 0.81\\
keyboard & 6.88\#6.62 & 6.33\#7.19 & 0.79 \\
sound & 7.12\#7.00 & 8.17\#8.23 & 1.62 \\
battery life & 3.50\#6.25 & 6.03\#7.16 & 2.69 \\
\midrule
\multicolumn{4}{l}{\textit{Restaurant domain}} \\
food & 7.50\#7.75 & 7.90\#8.15 & 0.57\\
service & 7.75\#7.75 & 7.55\#7.67 & 0.22\\
staff & 7.33\#7.67 & 7.28\#7.82 & 0.16\\
food & 7.75\#6.50 & 6.47\#7.11 & 1.42\\
\bottomrule
\end{tabular}
\caption{Subtask~1 (DimASR) sample predictions on the development set. VA format: Valence\#Arousal.}
\label{tab:subtask1_examples}
\end{table}

% [NEW] 5.2 — Subtask 2 results.
%   Table 3: Sample triplet extractions from test set
%   (pred_eng_laptop_sub2_new.jsonl, pred_eng_restaurant_sub2_new.jsonl).
%   DESS dev-set cF1 also reported (8.22%).
\subsection{Subtask~2: DimASTE}

For Subtask~2, the Pipeline-DeBERTa system extracts (Aspect, Opinion, VA) triplets.
Table~\ref{tab:subtask2_examples} shows sample predictions on the test set.
The system successfully identifies multi-word aspects and opinions, and produces VA scores that reflect sentiment polarity.
%
The DESS model achieves 8.22\% micro cF1 on the development set (precision 8.40\%, recall 8.06\%), with entity extraction proving more robust than VA prediction.



\begin{table}[t]
\centering
\small
\begin{tabular}{lll}
\toprule
\textbf{Aspect} & \textbf{Opinion} & \textbf{V\#A} \\
\midrule
\multicolumn{3}{l}{\textit{Laptop domain}} \\
battery life & perfect & 7.32\#7.12 \\
computer & highly recommend & 8.11\#7.98 \\
mac os & flawlessly & 6.98\#7.32 \\
laptop & great & 7.19\#7.50 \\
\midrule
\multicolumn{3}{l}{\textit{Restaurant domain}} \\
stewed chicken & excellent & 7.88\#7.73 \\
service & absolutely horrendous & 3.19\#7.53 \\
food & excellent & 8.10\#7.64 \\
server & super attentive & 5.75\#6.98 \\
\bottomrule
\end{tabular}
\caption{Subtask~2 (DimASTE) sample triplet extractions on the test set. Aspect-opinion pairs are shown; VA scores are predicted per pair.}
\label{tab:subtask2_examples}
\end{table}

% [NEW] 5.3 — Subtask 3 results.
%   Table 4: Sample quadruplet predictions from test set
%   (pred_eng_laptop_sub3_new.jsonl, pred_eng_restaurant_sub3_new.jsonl).
%   Shows aspect, opinion, and predicted category.
\subsection{Subtask~3: DimASQP}

For Subtask~3, the pipeline extends triplet extraction with category prediction.
Table~\ref{tab:subtask3_examples} shows sample quadruplet predictions.
The category classifier correctly assigns domain-specific categories in most cases (e.g., \texttt{FOOD\#QUALITY} for food items, \texttt{SERVICE\#GENERAL} for service aspects).


\begin{table}[t]
\centering
\resizebox{0.49\textwidth}{!}{
\begin{tabular}{lll}
\toprule
\textbf{Aspect} & \textbf{Opinion} & \textbf{Category} \\
\midrule
\multicolumn{3}{l}{\textit{Laptop domain}} \\
battery life & fairly low & \texttt{BATTERY\#OPERATION\_PERFORMANCE.} \\
fans & loud & \texttt{MULTIMEDIA\#OPERATION\_PERFORMANCE.} \\
laptop & great & \texttt{LAPTOP\#GENERAL} \\
\midrule
\multicolumn{3}{l}{\textit{Restaurant domain}} \\
stewed chicken & excellent & \texttt{FOOD\#QUALITY} \\
service & horrendous & \texttt{SERVICE\#GENERAL} \\
cafe & extremely dirty & \texttt{AMBIENCE\#GENERAL} \\
\bottomrule
\end{tabular}
}
\caption{Subtask~3 (DimASQP) sample quadruplet predictions on the test set, showing aspect, opinion, and predicted category.}
\label{tab:subtask3_examples}
\end{table}


\subsection{Analysis}

%\paragraph{Challenges of VA Regression in Span-Based Models.}
%The primary challenge lies in the fundamental mismatch between the D2E2S architecture's design for categorical classification and the continuous regression required for VA prediction.
%Several factors contribute to the low performance:

%\begin{enumerate}
%    \item \textbf{Label Space Explosion:} The original D2E2S treats each unique sentiment type as a discrete class.
%    When VA scores are encoded as strings (e.g., ``7.50\#7.62''), each unique VA pair becomes a separate ``class,'' resulting in thousands of unique types versus the original 3--4 sentiment categories.
%    This fundamentally breaks the evaluation framework's matching logic.

%    \item \textbf{Loss Scale Mismatch:} The MSE loss for VA regression operates on a different scale than the cross-entropy entity loss, requiring careful balancing.
%    The batch loss weighting factor of 10 was inherited from the original model and may not be optimal for the regression setting.

 %   \item \textbf{Negative Sampling:} The D2E2S training procedure uses negative sampling for entity and triplet candidates.
    %The negative sampling strategy, designed for categorical sentiment, may not provide appropriate training signal for continuous VA regression.

%    \item \textbf{Evaluation Framework Mismatch:} The original D2E2S evaluator performs exact string matching on sentiment types.
 %   With continuous VA predictions, exact matches are extremely unlikely, leading to artificially low reported scores even when predictions may be numerically close to gold values.
%\end{enumerate}

%\paragraph{VA Score Distribution.}
%Analysis of the training data reveals that VA scores are heavily skewed toward positive valence (mean~6.40) and high arousal (mean~7.13), reflecting the predominantly positive sentiment in restaurant and laptop reviews.
%This imbalanced distribution makes it difficult for the model to learn the full VA space.

Table~\ref{tab:va_r2_results} summarizes the R\textsuperscript{2} scores for Valence and Arousal prediction across the three subtasks. 
Subtask 1 achieves reasonable performance for Valence prediction (0.589), but the negative R\textsuperscript{2} value for Arousal (-0.349) indicates that the model performs worse than a mean-based baseline for this dimension. 
This suggests that arousal signals in Subtask 1 are more difficult to model, potentially due to weaker intensity cues or higher distributional variance. 
Subtask 2 demonstrates clear improvements in both Valence (0.659) and Arousal (0.412), reflecting the benefit of aspect-aware modeling and better contextual alignment. 
The strongest overall performance is observed in Subtask 3, which achieves the highest R\textsuperscript{2} values for both Valence (0.681) and Arousal (0.423), indicating improved stability and more effective fine-grained emotion prediction.

% [Change] Moved caption below table and standardized to booktabs style for consistency.
\begin{table}[t]
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Subtask} & \textbf{R\textsuperscript{2} (Valence)} & \textbf{R\textsuperscript{2} (Arousal)} \\
\midrule
Subtask 1 & 0.589 & -0.349 \\
Subtask 2 & 0.659 & 0.412 \\
Subtask 3 & 0.681 & 0.423 \\
\bottomrule
\end{tabular}
\caption{R\textsuperscript{2} performance for Valence (V) and Arousal (A) prediction across Subtasks 1--3.}
\label{tab:va_r2_results}
\end{table}

% [change.3] Added baseline comparison table to contextualize our results against official baselines.
Table~\ref{tab:baseline_comparison} compares our system's performance against the official baselines provided by the task organizers~\cite{yu-etal-2026-semeval} on the DimABSA 2026 dataset~\cite{lee2026dimabsa}.
Our system scores below both LLM-based baselines, which highlights the difficulty of adapting span-based categorical architectures to continuous VA regression compared to general-purpose large language models. Further analysis on errors can be found in Appendix~\ref{app:err}.

\begin{table}[t]
\centering
\begin{tabular}{lc}
\toprule
\textbf{System} & \textbf{Score} \\
\midrule
Ours (Team VYN) & 1.7978 \\
Baseline (Kimi-K2 Thinking) & 2.1461 \\
Baseline (Qwen-3 14B) & 2.6427 \\
\bottomrule
\end{tabular}
\caption{Comparison with official baselines on the DimABSA 2026 shared task.And this is specifically for Restaurant domain English dataset}
\label{tab:baseline_comparison}
\end{table}

\section{Discussion}

\paragraph{Lessons Learned.}
Adapting a categorical span-extraction model to continuous regression is more challenging than a simple head replacement.
The entire training pipeline (including negative sampling, loss balancing, and evaluation) needs to be redesigned for the regression setting.

Future work should consider:

\begin{itemize}
    \item Decoupling entity extraction from VA regression, training the entity extractor first and then fine-tuning the VA head
    \item Using a dedicated regression loss such as Smooth L1 or Huber loss instead of MSE
    \item Implementing the continuous F1 metric directly as a training signal or for model selection
    \item Exploring binned VA prediction as an intermediate approach between classification and regression
\end{itemize}


\section{Conclusion}

We presented Team VYN's system for the DimABSA 2026 Shared Task, addressing all three Track~A subtasks.
Our approach combines a dedicated DeBERTa regression model for Subtask~1, an MRC-based pipeline for Subtasks~2 and~3, and DESS as an alternative for Subtask~2.
Our experiments highlight the fundamental challenges of transitioning from categorical to continuous dimensional sentiment in structured ABSA tasks.
Key difficulties include VA label space explosion in span-based models, loss balancing between extraction and regression objectives, and biased VA distributions in training data.
Future work should explore architectures specifically designed for joint span extraction and continuous regression, decoupled training strategies, and data augmentation to address VA distribution imbalance.
The system achieves 8.22\% micro F1 for triplet extraction (Subtask~2) on the development set, indicating substantial room for improvement.
We believe that architectures specifically designed for joint span extraction and continuous regression, rather than adapted classification models, will be necessary to advance performance on this challenging task.

\section*{Limitations}

Our work has several limitations.
First, we only evaluate on English data, while the DimABSA task covers multiple languages.
Second, our DESS adaptation treats VA prediction as a simple head replacement without deeper architectural changes to accommodate regression. Third, computational constraints limited our hyperparameter search, and the model was trained on Kaggle T4 GPUs with small batch sizes. Finally, the evaluation framework inherited from D2E2S uses exact string matching for sentiment types, which is fundamentally incompatible with continuous VA predictions and likely underestimates actual model performance. Limitation in the contect of LLMs in discussed in Appendix~\ref{app:llm_discussion}.



\section*{Ethics Statement}

This work uses publicly available datasets from the DimABSA 2026 shared task.
The restaurant and laptop review data does not contain personally identifiable information.
Our models are trained for research purposes and are not intended for deployment in production systems without further validation.

\section*{Acknowledgments}

We thank the DimABSA 2026 organizers for providing the dataset and evaluation framework.
We also acknowledge the authors of the D2E2S model for making their code publicly available.
Computational resources were provided by Kaggle.

%\bibliographystyle{plainnat}
%\bibliography{anthology-1,anthology-2,references}
\bibliography{anthology.min,references}

\appendix

% [Change] Added detailed DESS adaptation methodology to appendix (Reviewer 1 - insufficient methodology detail).
\section{DESS Architecture Adaptation Details}
\label{app:dess_adaptation}

\begin{figure*}[!htb]
\centering
\includegraphics[width=0.85\textwidth]{ReseachPapers/Images/FigSubtask1.png}
\caption{Enlarged: Architecture of the Subtask~1 (DimASR) regression model (same as Figure~\ref{fig:subtask1_arch}).}
\label{fig:subtask1_arch_large}
\end{figure*}

\begin{figure*}[!htb]
\centering
\includegraphics[width=0.85\textwidth]{ReseachPapers/Images/FigSubtask2.png}
\caption{Enlarged: Architecture of the Subtask~2 (DimASTE) Pipeline-DeBERTa system (same as Figure~\ref{fig:subtask2_arch}).}
\label{fig:subtask2_arch_large}
\end{figure*}

\begin{figure*}[!htb]
\centering
\includegraphics[width=0.85\textwidth]{ReseachPapers/Images/FigSubtask3.png}
\caption{Enlarged: Architecture of the Subtask~3 (DimASQP) quad prediction system (same as Figure~\ref{fig:subtask3_arch}).}
\label{fig:subtask3_arch_large}
\end{figure*}

The DESS model is adapted from the D2E2S architecture~\cite{thenuwara2025dess}, originally designed for categorical ASTE. Below we detail the specific modifications made for continuous VA regression.

\paragraph{GCN Layer Modifications.}
The dual-channel GCN layers (Syntactic and Semantic) remain structurally unchanged from D2E2S, as their role is to produce enriched token representations rather than directly predict sentiment. Both channels use 2-layer GCNs with 768-dimensional hidden states. The Syntactic GCN operates on the spaCy-generated dependency parse adjacency matrix, while the Semantic GCN uses multi-head self-attention (4 heads) to learn a soft adjacency matrix capturing implicit semantic relationships.

\paragraph{Output Layer Modifications.}
The key architectural change is in the sentiment prediction head. In the original D2E2S, this head is a linear classifier that maps a concatenated representation of the aspect span, opinion span, context (CLS token), and size embeddings to one of 3--4 discrete sentiment classes (positive, negative, neutral). For VA regression, we replace this classifier with a two-layer MLP regression head:
\begin{equation}
\begin{split}
[\hat{V}, \hat{A}] = W_2 \cdot \text{GELU}(W_1 \cdot [h_{\text{asp}}; h_{\text{opn}}; \\
  h_{\text{ctx}}; e_s] + b_1) + b_2
\end{split}
\end{equation}
where $h_{\text{asp}}$ and $h_{\text{opn}}$ are max-pooled span representations of the aspect and opinion entities, $h_{\text{ctx}}$ is the CLS token representation, $e_s$ is a concatenation of learned size embeddings for both spans, $W_1 \in \mathbb{R}^{256 \times d}$ and $W_2 \in \mathbb{R}^{2 \times 256}$ are learned weight matrices, and GELU is the activation function. The output is clamped to $[1, 9]$ during inference.

\paragraph{Loss Function Adaptation.}
The original D2E2S uses cross-entropy loss for the sentiment head. In the DESS adaptation, the sentiment head retains cross-entropy over string-encoded VA labels (e.g., ``7.50\#7.62'' treated as a discrete class), which led to the label space explosion discussed in the main text. In contrast, the Pipeline-DeBERTa system uses MSE loss for VA regression, avoiding this issue.

% [Change] Added subtask-specific architecture summaries to appendix (Reviewer 2 - system overview difficult to follow).
% [Change] Added enlarged figures in appendix for better readability (Reviewer 2 and Reviewer 3 - figures too small).

\section{Subtask Architecture Summaries}
\label{app:arch_summaries}

Table~\ref{tab:arch_summary} provides a structured comparison of the three subtask architectures, including their inputs, outputs, loss functions, and prediction pipelines.

\begin{table*}[!htb]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{lp{3cm}p{3cm}p{3cm}p{3cm}}
\toprule
 & \textbf{Model} & \textbf{Input / Output} & \textbf{Loss Function} & \textbf{Pipeline} \\
\midrule
\textbf{Subtask 1} (DimASR) & DeBERTa-v3-base + MLP regression head & Input: \texttt{[CLS] aspect [SEP] text [SEP]} \newline Output: (Valence, Arousal) $\in [1,9]^2$ & MSE loss on VA predictions & Single forward pass: encode text--aspect pair $\to$ CLS representation $\to$ MLP $\to$ VA scores \\
\midrule
\textbf{Subtask 2} (DimASTE) & Pipeline-DeBERTa with 7 task-specific heads & Input: raw text \newline Output: (Aspect, Opinion, VA) triplets & Cross-entropy for extraction heads + MSE for VA heads & Bidirectional MRC extraction (A$\to$AO, O$\to$OA) $\to$ cross-validation ($\beta{=}0.90$) $\to$ VA regression \\
\midrule
\textbf{Subtask 3} (DimASQP) & Pipeline-DeBERTa + Category head & Input: raw text \newline Output: (Aspect, Category, Opinion, VA) quadruplets & Subtask 2 loss + $\beta \cdot \mathcal{L}_{\text{cat}}$ & Subtask 2 pipeline + category classification via MRC query \\
\bottomrule
\end{tabular}
}
\caption{Structured comparison of architectures across the three subtasks.}
\label{tab:arch_summary}
\end{table*}

\paragraph{Subtask 1 (DimASR).}
This subtask receives pre-identified aspects and predicts VA scores. The architecture is a straightforward regression model: a DeBERTa-v3-base encoder processes the concatenated aspect--text input, and the CLS token representation is passed through a two-layer MLP (768$\to$256$\to$2) with GELU activation and dropout (0.1) to produce Valence and Arousal predictions. 
%
Figure~\ref{fig:subtask1_arch_large}, which is a scaled up representation of Figure~\ref{fig:subtask1_arch}, shows the Subtask 1 architecture. 

\paragraph{Subtask 2 (DimASTE).}
This subtask requires extracting (Aspect, Opinion, VA) triplets from raw text. The Pipeline-DeBERTa system uses a shared encoder feeding seven task-specific heads. Bidirectional MRC extraction identifies aspect--opinion pairs: a forward pass extracts opinions given aspects, and a backward pass extracts aspects given opinions. Cross-validation with a confidence threshold ($\beta{=}0.90$) filters spurious pairings. Validated pairs are then scored by dedicated Valence and Arousal regression heads.
%
Figure~\ref{fig:subtask2_arch_large}, which is a scaled up representation of Figure~\ref{fig:subtask2_arch}, shows the Subtask 2 architecture.

\paragraph{Subtask 3 (DimASQP).}
This subtask extends Subtask 2 by additionally predicting aspect categories. The architecture activates a Category classification head that assigns each validated aspect--opinion pair to a domain-specific taxonomy (e.g., \texttt{FOOD\#QUALITY}, \texttt{SERVICE\#GENERAL}) via an MRC query.
%
Figure~\ref{fig:subtask3_arch_large}, which is a scaled up representation of Figure~\ref{fig:subtask3_arch}, shows the Subtask 3 architecture.

% [Change] Added definition and justification for L_batch and its weight of 10.
%\section{Training Loss Details}
%\label{app:loss}

%The batch regularization loss $\mathcal{L}_{\text{batch}}$ in Eq.~\ref{eq:batch} penalizes high variance in VA predictions within each training batch, encouraging stable and consistent predictions:
%\begin{equation}
%\mathcal{L}_{\text{batch}} = \text{Var}(\hat{V}_{\text{batch}}) + \text{Var}(\hat{A}_{\text{batch}})
%\label{eq:batch}
%\end{equation}

%The weight factor of 10 was determined through preliminary experiments on the development set; without this scaling, the batch regularization term had negligible effect on training dynamics due to its small magnitude relative to the entity and VA losses.

% --- 4.1 Data ---
% DimABSA 2026 English dataset, Track A. Combined restaurant + laptop domains.
% Table 1: 3,727 train / 400 test samples.
\section{Data}
\label{app:data}

We use the DimABSA 2026 English dataset \cite{lee2026dimabsa} for Track~A.
Table~\ref{tab:data_stats1} summarizes the dataset statistics.

\begin{table}[!htb]
\centering
\small
\begin{tabular}{lrr}
\toprule
\textbf{Statistic} & \textbf{Train} & \textbf{Test} \\
\midrule
Samples & 3,727 & 400 \\
Domains & \multicolumn{2}{c}{Restaurant + Laptop} \\
Avg.\ entities/sample & 3.06 & -- \\
Avg.\ sentiments/sample & 1.53 & -- \\
\bottomrule
\end{tabular}
\caption{Dataset statistics for the combined English training and test sets.}
\label{tab:data_stats1}
\end{table}

% --- 4.3 Preprocessing ---
% DimABSA JSONL -> DESS JSON conversion using spaCy v4.0.
% 3,712 of 3,727 samples successfully converted (15 lost to alignment failures).
\section{Preprocessing}
\label{app:pre}

For the DESS model, we convert the DimABSA JSONL data to the DESS JSON format using spaCy (v4.0) for tokenization, POS tagging, and dependency parsing.
Entity spans are aligned to token boundaries.
The combined restaurant and laptop training data yields 3,712 successfully converted samples (15 samples lost due to tokenization alignment failures).
%
The following shows the conversion from DimABSA JSONL format to DESS JSON format:

\paragraph{DimABSA Input:}
{\small
\begin{verbatim}
{"ID": "R001",
 "Text": "The food was great.",
 "Triplet": [
   {"Aspect": "food",
    "Opinion": "great",
    "VA": "7.50#7.62"}]}
\end{verbatim}
}

\paragraph{DESS Output:}
{\small
\begin{verbatim}
{"tokens": ["The","food","was","great"],
 "entities": [
   {"type":"target","start":1,"end":2},
   {"type":"opinion","start":3,"end":4}],
 "sentiments": [
   {"type":"7.50#7.62",
    "head":0,"tail":1}],
 "pos": ["DET","NOUN","AUX","ADJ"],
 "dependency": [[0,1],[1,2],...]}
\end{verbatim}
}


% --- 4.2 Hyperparameters ---
% Table 2: Key hyperparameters for the DESS model. Trained on Kaggle T4 GPUs.
\section{Hyperparameters}
\label{app:hyper}

Table~\ref{tab:hyperparams} lists the key hyperparameters for the DESS model.
We train on Kaggle T4 GPUs.

\begin{table}[!htb]
\centering
\small
\begin{tabular}{lr}
\toprule
\textbf{Parameter} & \textbf{Value} \\
\midrule
Encoder & DeBERTa-v3-base \\
Encoder dim & 768 \\
BiLSTM layers & 2 \\
BiLSTM hidden dim & 384 \\
GCN layers & 2 \\
Max span size & 8 \\
Batch size & 4 \\
Epochs & 10 \\
Learning rate & $5 \times 10^{-5}$ \\
LR warmup & 0.2 \\
Weight decay & 0.01 \\
Max grad norm & 1.0 \\
Dropout & 0.1 \\
GCN dropout & 0.2 \\
Neg.\ entity samples & 100 \\
Neg.\ triple samples & 100 \\
Seed & 42 \\
\bottomrule
\end{tabular}
\caption{Hyperparameters for the DESS model.}
\label{tab:hyperparams}
\end{table}










\section{Error Analysis}
\label{app:err}

We observe several recurring error patterns.
For the DESS model, the primary challenge is the mismatch between the architecture's original design for categorical classification and the continuous regression required for VA prediction.
Encoding VA scores as strings (e.g., ``7.50\#7.62'') causes each unique VA pair to become a separate ``class,'' resulting in thousands of unique types versus the original 3--4 sentiment categories.
This label space explosion fundamentally limits the span-based model's ability to learn meaningful VA representations.

For the Pipeline-DESS approach, error propagation across pipeline stages is the main concern: incorrect aspect or opinion extraction in early stages cascades into incorrect VA predictions.
Additionally, arousal prediction proves consistently harder than valence across all subtasks, as reflected by the negative R\textsuperscript{2} for arousal in Subtask~1.
This may stem from arousal being a less lexically grounded dimension than valence in review text.


%\section{Data Conversion Example}
%\label{app:data_example}




%


% [Change] Added LLM discussion to limitations appendix (Reviewer 1 - missing discussion of modern LLM architectures).
\section{Discussion on LLM-Based Approaches}
\label{app:llm_discussion}

A limitation of our work is the absence of comparison with modern LLMs based approaches. Recent work has shown that LLMs such as GPT-4, Llama-3, and Qwen can achieve competitive performance on ABSA tasks through zero-shot and few-shot prompting, without task-specific fine-tuning. The official DimABSA 2026 baselines included LLM-based systems (Qwen, KIMI), which we did not benchmark against.

Given that our work focused on English data, zero-shot or few-shot prompting with accessible LLMs (e.g., GPT-4o-mini, Llama-3-8B) would have been a feasible and informative baseline. Such approaches could potentially bypass the label space explosion issue entirely by generating VA scores as continuous numbers in natural language output, rather than requiring architectural modifications to classification models.

Our decision to focus on span-based architectures was motivated by the desire to explore whether established ASTE models could be adapted to dimensional sentiment. However, we acknowledge that a brief LLM comparison would have provided valuable context for interpreting our results. Future work should include LLM baselines to better understand the trade-offs between fine-tuned specialized models and general-purpose LLMs for dimensional ABSA.

% [Change] Added enlarged figures in appendix for better readability (Reviewer 2 and Reviewer 3 - figures too small).
%\section{Enlarged Architecture Figures}
%\label{app:enlarged_figures}

%The following figures reproduce the architecture diagrams from the main text at larger scale for improved readability: Figure~\ref{fig:subtask1_arch_large} (Subtask~1), Figure~\ref{fig:subtask2_arch_large} (Subtask~2), and Figure~\ref{fig:subtask3_arch_large} (Subtask~3).



\end{document}
