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\begin{document}

\title{NTNU-SMIL at SemEval-2026 Task 3: Logistic-Loss Regression with Same-Language Transfer for Valence--Arousal Stance Prediction in Dimensional Stance Analysis (DimStance)}


\author{
\begin{tabular}{c}
Siang-Ting Lin, Tien-Hong Lo, Yun-Ting Sun, Jhih-Rong Guo, Tung-Yen Hao, \\
Fong-Chun Tsai, and Berlin Chen
\end{tabular} \\
The Speech and Machine Intelligence Laboratory (SMIL) \\
Department of Computer Science and Information Engineering \\
National Taiwan Normal University, Taiwan \\
\texttt{\{61347114S, teinhonglo, 61347015s, jhihrong, 41247050, fongchun.tsai, } \\
\texttt{berlin\}@ntnu.edu.tw}
}

\maketitle

\begin{abstract}
Dimensional Stance Analysis (DimStance) requires aspect-conditioned regression of valence and arousal (VA) across languages and domains, where distribution shift often causes scale mismatch and variance shrinkage, especially for arousal.
We describe NTNU-SMIL's system for SemEval-2026 Task~3 Track~B (DimStance) Subtask~1 (DimASR) \citep{yu-etal-2026-semeval,becker2026dimstancemultilingualdatasetsdimensional}.
Our model uses sentence-pair encoding (\texttt{[CLS] Text [SEP] Aspect [SEP]}), dual VA heads, and a logistic-loss regression objective implemented by min--max normalizing labels to $[0,1]$ and mapping logits back to $[1,9]$ via a sigmoid.
For English and Chinese, we further apply same-language transfer by pretraining on Track~A and fine-tuning on Track~B \citep{lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
We add lightweight out-of-fold linear calibration and multi-seed ensembling to reduce scale mismatch under shift \citep{guo2017calibration,kuleshov2018accurate,lakshminarayanan2017deepensembles}.
Post-hoc analysis on the released test gold indicates that same-language transfer and logistic-loss regression account for most of the gains in English and Chinese, while calibration provides secondary adjustments; arousal variance collapse remains a key failure mode in lower-resource settings such as Swahili.
\end{abstract}

\section{Introduction}

Traditional stance detection is typically formulated as a categorical classification problem (favor/against/neutral), as in SemEval-2016 Task~6 \citep{mohammad2016semeval}.
Subsequent work expanded stance modeling to target-conditioned and cross-lingual settings \citep{augenstein2016stance,vamvas2020xstance}.
However, most prior benchmarks remain label-discrete and do not capture fine-grained intensity variation.

Dimensional affect modeling instead represents affect using continuous variables such as valence and arousal \citep{mehrabian1974approach,russell1980circumplex,bradley1994measuring}.
SemEval-2026 Task~3 introduces DimABSA, a multilingual, multi-domain benchmark for aspect-level VA prediction \citep{yu-etal-2026-semeval,lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
DimStance further extends the dimensional paradigm from sentiment to stance, requiring aspect-conditioned VA regression across languages and domains \citep{becker2026dimstancemultilingualdatasetsdimensional}.
In Track~B, Subtask~1 (DimASR) corresponds to target-conditioned valence--arousal regression for stance, where the stance target is treated as an aspect.

As illustrated in Figure~\ref{fig:dimabsa}, DimStance predicts aspect-specific VA scores and is highly sensitive to cross-lingual distribution shift.
Even strong multilingual encoders can yield miscalibrated scales and under-dispersed predictions under cross-lingual transfer, which degrades both RMSE and correlation \citep{guo2017calibration,kuleshov2018accurate,ovadia2019can}.
This issue is often most severe for arousal, especially in low-resource languages.

\begin{figure}[ht]
\centering
\small

% ----- Top VA numbers -----
\makebox[0pt][l]{\hspace{-3cm}\textcolor{orange}{7.00\#6.00}}%
\makebox[0pt][l]{\hspace{2.2cm}\textcolor{orange}{4.83\#3.83}}%
\\

% ----- Top i symbols -----
\makebox[0pt][l]{\hspace{-2.5cm}\textcolor{orange}{$i_1$}}%
\makebox[0pt][l]{\hspace{2.7cm}\textcolor{orange}{$i_2$}}%

% ----- Sentence -----
S: \textcolor{teal}{Nuclear energy} plays a role in mitigating
\textcolor{teal}{climate change}.\\[-2pt]

% ----- Bottom a symbols -----
\makebox[0pt][l]{\hspace{-2.5cm}\textcolor{teal}{$a_1$}}%
\makebox[0pt][l]{\hspace{2.7cm}\textcolor{teal}{$a_2$}}%

\vspace{0.6em}

% ----- Table -----
\begin{tabular}{lccc}
\toprule
\textbf{Subtask} & \textbf{Input} & \textbf{Output} & \textbf{Task Type} \\
\midrule
\multirow{2.5}{*}{VA Prediction}
& $S + \textcolor{teal}{a_1}$ & $\{\textcolor{orange}{i_1}\}$ & \multirow{2.5}{*}{Regression} \\
\cmidrule(lr){2-3}
& $S + \textcolor{teal}{a_2}$ & $\{\textcolor{orange}{i_2}\}$ & \\
\bottomrule
\end{tabular}

\caption{
Illustration of DimStance Subtask~1 (DimASR) as aspect-conditioned valence--arousal regression.
Given a text $S$ and a target/aspect $a_j$, the system predicts a VA pair $i_j=(V_j, A_j)$.
}

\label{fig:dimabsa}
\end{figure}

In this paper, we focus on English and Chinese, the two languages for which Track~A provides same-language cross-domain supervision \citep{lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
Our system combines sentence-pair encoding, a final-layer \texttt{[CLS]} representation, dual regression heads, logistic-loss regression, same-language transfer, and lightweight calibration-aware ensembling; post-hoc analysis suggests that same-language transfer and logistic-loss regression account for most of the gains.

\section{Related Work}

\subsection{Stance Detection and Aspect/Target Conditioning}
Early stance detection benchmarks mainly cast the task as categorical classification, with SemEval-2016 Task~6 as a representative shared task \citep{mohammad2016semeval}.
Target-conditioned encoding has been shown to be effective for stance prediction when target/aspect information is explicitly provided \citep{augenstein2016stance}.
Recent benchmarks further emphasize multilingual and cross-target stance modeling \citep{vamvas2020xstance}.

\subsection{Dimensional Affect Modeling and Dimensional ABSA/Stance}
Dimensional affect modeling represents affect using continuous variables such as valence and arousal \citep{mehrabian1974approach,russell1980circumplex,bradley1994measuring}.
DimABSA provides multilingual, multi-domain aspect-level VA annotations for dimensional sentiment learning \citep{yu-etal-2026-semeval,lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
DimStance extends the dimensional paradigm from sentiment to stance by requiring aspect-conditioned VA regression across languages and domains \citep{becker2026dimstancemultilingualdatasetsdimensional}.

\subsection{Calibration and Ensembling for Robust Regression}
Calibration has been widely studied for modern neural networks \citep{guo2017calibration}.
For regression, calibrated approaches are known to improve reliability under distribution shift \citep{kuleshov2018accurate}.
Ensembling is a strong and simple strategy for improving generalization and predictive stability \citep{lakshminarayanan2017deepensembles}, and uncertainty under shift has been empirically analyzed for deep models \citep{ovadia2019can}.
Motivated by these observations, our system combines aspect-conditioned encoding, dimension-specific heads, same-language cross-domain pretraining for English and Chinese, and lightweight post-hoc calibration for robust VA regression.

\section{Methods}
\label{sec:methods}

\subsection{Overall Architecture}
\label{sec:architecture}
Our approach follows a two-stage training strategy:
(1) pretraining on Track~A for aspect-aware dimensional regression (English and Chinese only), and
(2) fine-tuning on Track~B with dual-head regression and a logistic-loss regression formulation.

Figure~\ref{fig:architecture} illustrates the overall architecture of our system.
\begin{figure}[ht]
    \centering
    \includegraphics[width=\columnwidth]{figures/Architecture.png}

    \caption{
    Overall architecture of our system.
    Left: same-language pretraining on Track~A for English and Chinese only.
    Right: fine-tuning on Track~B with sentence-pair encoding and two regression heads for valence and arousal.
    }

    \label{fig:architecture}
\end{figure}

\paragraph{Sentence-Pair Encoding.}
For both training stages, we construct sentence-pair inputs in the form
\texttt{[CLS] Text [SEP] Aspect [SEP]}.
This formulation allows the backbone encoder to model text--aspect interactions implicitly through self-attention over the concatenated sequence.

\paragraph{Backbone Encoder.}
We employ a pre-trained transformer backbone, with the exact model chosen per language (Appendix~\ref{sec:appendix_backbones}).
The encoder produces contextualized token representations for the joint text--aspect sequence.
We use the final-layer \texttt{[CLS]} token embedding as the aggregated aspect-aware representation for downstream valence and arousal prediction.

\paragraph{Dual-Head Regression Design.}
Valence and arousal are modeled using two independent linear heads.
Let $\mathbf{h}$ denote the final hidden representation. The predictions are computed as:
\[
z_V = \mathbf{w}_v^\top \mathbf{h} + b_v,
\quad
z_A = \mathbf{w}_a^\top \mathbf{h} + b_a.
\]

In our implementation, these head outputs are treated as logits and are mapped back to the original score range via a sigmoid transformation.

\paragraph{Logistic-Loss Regression Formulation.}
Instead of directly optimizing mean squared error, we apply min--max normalization to the gold scores:
\[
y' = \frac{y - 1}{8},
\quad y \in [1,9], \; y' \in [0,1].
\]
We then optimize the regression objective using binary cross-entropy on continuous soft targets:
\[
\mathcal{L}_{\text{BCE}} =
- \left( y' \log \sigma(z)
+ (1 - y') \log (1 - \sigma(z)) \right),
\]
where $\sigma(\cdot)$ denotes the sigmoid function.
Under this formulation, the model learns logits whose sigmoid outputs correspond to normalized continuous scores rather than discrete class probabilities.


\paragraph{Two-Stage Training Strategy (English and Chinese).}

In Stage 1, the model is pretrained on Track~A data (English and Chinese only) to learn aspect-aware dimensional representations \citep{lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
In Stage 2, the model is fine-tuned on Track~B data with the same regression formulation.

\paragraph{OOF Calibration and Ensembling.}
To reduce scale mismatch under distribution shift, we fit a lightweight linear calibration model on out-of-fold (OOF) predictions from 5-fold training splits, and apply it to the corresponding test predictions \citep{kuleshov2018accurate}.
Final predictions are obtained by averaging calibrated outputs across folds and random seeds \citep{lakshminarayanan2017deepensembles}.

\section{Experiments}

\subsection{Experimental Settings}

\paragraph{Datasets}
Table~\ref{tab:dataset_stats_used} summarizes the dataset statistics used in our experiments.

\begin{table}[ht]
\centering
\small
\begin{tabular}{l l r r}
\toprule
\textbf{Track} & \textbf{Language} & \textbf{Train Inst.} & \textbf{Dev Inst.} \\
\midrule
A  & ENG & 9,432 & 615 \\
A  & ZHO & 17,658 & 1,806 \\
\midrule
B  & ENG & 2,059 & 339 \\
B  & DEU & 1,335 & 75 \\
B  & ZHO & 1,091 & 149 \\
B  & PCM & 1,118 & 122 \\
B  & SWA & 1,622 & 145 \\
\bottomrule
\end{tabular}


\caption{
Dataset statistics used in our experiments.
Track~A training data are used only for same-language pretraining (English and Chinese).
}


\label{tab:dataset_stats_used}
\end{table}

\paragraph{Data Usage and Splitting Strategy.}
Although we officially participate only in Track~B (DimStance), we leverage Track~A (DimABSA) Subtask~1 data for same-language supervised pretraining for English and Chinese \citep{yu-etal-2026-semeval,lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
For Track~B, we perform 5-fold cross-validation on training data with sentence-level grouping to avoid leakage across aspects.

Out-of-fold (OOF) predictions from 5-fold training are used for linear calibration and ensembling. Calibration is performed exclusively on Track~B training data, and no test labels are accessed during training, calibration, or model selection. The full protocol is summarized in Table~\ref{tab:appendix_protocol} in Appendix~\ref{sec:appendix_protocol}.

\paragraph{Evaluation Metrics.}
For Track~B Subtask~1 (DimASR), we follow the official evaluation metric defined in the DimStance dataset paper \citep{becker2026dimstancemultilingualdatasetsdimensional}. 
System performance is measured using Root Mean Square Error (RMSE) in the valence--arousal (VA) space:

{\small
\begin{equation}
\label{eq:rmse_va}
\begin{aligned}
\mathrm{RMSE}_{VA}
&=
\sqrt{
\sum_{i=1}^{N}
\frac{
\left(V_{p}^{(i)}-V_{g}^{(i)}\right)^{2}
+
\left(A_{p}^{(i)}-A_{g}^{(i)}\right)^{2}
}{N}
}.
\end{aligned}
\end{equation}
\vspace{0.2em}
}

where $N$ is the total number of instances; $V_{p}^{(i)}$ and $A_{p}^{(i)}$ denote the predicted valence and arousal values for instance $i$, respectively; and $V_{g}^{(i)}$ and $A_{g}^{(i)}$ denote the corresponding gold valence and arousal values.

We also report Pearson correlation coefficients (PCC) for valence and arousal.

\paragraph{Training Objective.}
We model valence and arousal with two independent logits $z_V$ and $z_A$.
Gold scores $y \in [1,9]$ are min--max normalized as $\tilde{y} = (y - 1)/8 \in [0,1]$.
Predictions are mapped back to the original scale via $\hat{y} = 8\sigma(z) + 1$.
Training uses binary cross-entropy on the normalized soft targets, as described in Section~\ref{sec:architecture}.

\paragraph{Hyperparameters.}
All hyperparameter settings are listed in Table~\ref{tab:hparams} in Appendix~\ref{sec:appendix_hparams}.

% =========================
% NOTE: baseline table MOVED to Results (after main_results)
% =========================

\begin{table*}[ht]
\centering
\small
\setlength{\tabcolsep}{5pt}
\begin{tabular}{ll lllll c}
\toprule
\textbf{Category} & \textbf{System} & \textbf{ENG} & \textbf{DEU} & \textbf{ZHO} & \textbf{PCM} & \textbf{SWA} & \textbf{AVG} \\
\midrule
\multirow{5}{*}{PLM Regressors}
  & RemBERT                 & 1.993 & 2.262 & 0.795 & 2.149 & 2.322 & 1.904 \\
  & LaBSE                   & 2.288 & 1.918 & 0.715 & 1.720 & 2.103 & 1.749 \\
  & XLM-R                   & 2.005 & 1.667 & 0.747 & 1.732 & 2.054 & 1.641 \\
  & mBERT$^\dagger$         & 2.699 & 2.329 & 1.276 & 3.215 & 2.784 & 2.461 \\
\midrule
\multirow{3}{*}{\shortstack[l]{Closed LLMs\\(Prompting, 16-shot)}}
  & GPT-5 mini              & 1.819 & 1.754 & 1.492 & 1.389 & 2.321 & 1.755 \\
  & Gemini-2.5 Flash        & 1.771 & 1.701 & 1.284 & 1.353 & 2.259 & 1.674 \\
  & Kimi K2                 & 1.643 & 1.671 & 1.044 & 1.284 & 2.242 & 1.577 \\
\midrule
\multirow{4}{*}{\shortstack[l]{LLM Regressors\\(Fine-tuning)}}
  & Mistral-3 14B$^\dagger$ & 1.643 & 1.591 & 0.740 & 1.739 & 2.299 & 1.602 \\
  & Phi-4 14B               & 1.575 & 1.535 & 0.691 & 1.409 & 2.055 & 1.453 \\
  & Qwen-2.5 72B            & 1.520 & 1.493 & 0.681 & 1.228 & 2.012 & 1.387 \\
  & Llama-3.3 70B           & \textbf{1.468} & 1.415 & 0.679 & \textbf{1.157} & \textbf{1.859} & \textbf{1.316} \\
\midrule[\heavyrulewidth]
PLM Regressors & \textbf{Ours (NTNU-SMIL)} & 1.521 & \textbf{1.347} & \textbf{0.556} & 1.567 & 1.960 & 1.390 \\
\bottomrule
\end{tabular}

\caption{
Comparison with baseline and reference systems on the official test set (RMSE$_{VA}$; lower is better).
$^\dagger$mBERT and Mistral-3 14B are official baselines.
Bold indicates the best score for each language and the overall average within this table.
}


\label{tab:baseline}
\end{table*}

% Stop Results floats from drifting into later sections

\section{Results}
Table~\ref{tab:main_results} presents the results of our official final submission on the Track~B test set, evaluated using the official evaluation script. RMSE$_{VA}$ is the ranking metric, and PCC$_V$ and PCC$_A$ measure linear association.

Our system performs best on the Chinese test set (ZHO; RMSE$_{VA}$=0.5561), whereas the Swahili test set (SWA) shows the largest degradation, particularly for arousal (PCC$_A$=-0.0088).

\begin{table}[ht]
\centering
\small
\begin{tabular}{l l c c c}
\toprule
\textbf{Lang} & \textbf{Dom} & \textbf{RMSE$_{VA}$ ↓} & \textbf{PCC$_V$ ↑} & \textbf{PCC$_A$ ↑} \\
\midrule
ENG & Env & 1.5207 & 0.8016 & 0.4778 \\
DEU & Pol & 1.3467 & 0.8736 & 0.6339 \\
ZHO & Env & \textbf{0.5561} & \textbf{0.9127} & \textbf{0.6592} \\
PCM & Pol & 1.5674 & 0.8745 & 0.6175 \\
SWA & Pol & 1.9602 & 0.6763 & -0.0088 \\

\bottomrule
\end{tabular}

\caption{
Official test-set results for Track~B Subtask~1 (DimASR), evaluated using the official RMSE$_{VA}$ metric.
RMSE$_{VA}$ is the ranking metric (lower is better).
}
\label{tab:main_results}
\end{table}
Table~\ref{tab:baseline} compares our approach with official baselines and other reference results reported in the shared task materials \citep{becker2026dimstancemultilingualdatasetsdimensional}.
Although large LLM regressors achieve lower average RMSE$_{VA}$, our PLM-based system remains competitive, especially on DEU and ZHO, highlighting the effectiveness of task-specific regression design and calibration-aware ensembling.

\section{Analysis}

\subsection{Same-Language Cross-Domain Transfer (English and Chinese)}
\label{sec:ablation_pretrain}

To examine whether same-language cross-domain knowledge helps DimStance, we compare two official submission settings:
(i) \textbf{Direct FT}, trained only on Track~B, and
(ii) \textbf{A$\rightarrow$B FT}, pretrained on Track~A and then fine-tuned on Track~B.
Table~\ref{tab:ablation_transfer} reports the corresponding official test-set results and shows consistent gains from Track~A pretraining for both English and Chinese \citep{lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.

\begin{table}[ht]
\centering
\small
\setlength{\tabcolsep}{5pt}
\begin{tabular}{l l c c c}
\toprule
\textbf{Lang} & \textbf{Setting} & \textbf{RMSE$_{VA}$ ↓} & \textbf{PCC$_V$ ↑} & \textbf{PCC$_A$ ↑} \\
\midrule
\multirow{2}{*}{ENG}
& Direct FT & 1.6163 & 0.7683 & 0.4447 \\
& A$\rightarrow$B FT & \textbf{1.5207} & \textbf{0.8016} & \textbf{0.4778} \\
\midrule
\multirow{2}{*}{ZHO}
& Direct FT & 0.6392 & 0.8732 & 0.5360 \\
& A$\rightarrow$B FT & \textbf{0.5561} & \textbf{0.9127} & \textbf{0.6592} \\
\bottomrule
\end{tabular}

\caption{
Same-language cross-domain transfer (Track~A $\rightarrow$ Track~B) for English and Chinese.
\textbf{Direct FT} denotes training on Track~B only.
\textbf{A$\rightarrow$B FT} denotes Track~A pretraining followed by Track~B fine-tuning.
Both settings correspond to official submissions evaluated on the official test set.
}
\label{tab:ablation_transfer}
\end{table}

\begin{table*}[ht]
\centering
\small
\setlength{\tabcolsep}{4.5pt}
\begin{tabular}{l c c c c c}
\toprule
\textbf{Setting (scored on released test gold)} & \textbf{ENG} & \textbf{DEU} & \textbf{ZHO} & \textbf{PCM} & \textbf{SWA} \\
\midrule
MSE + 5-fold CV & 1.7095 & \textbf{1.3555} & 0.6923 & 1.4845 & 1.8940 \\
MSE + 5-fold CV + OOF calib & 1.7095 & 1.3569 & 0.6923 & 1.4845 & \textbf{1.8928} \\
BCE + 5-fold CV + OOF calibration & \textbf{1.6163} & 1.3762 & \textbf{0.6392} & 1.5406 & 2.0627 \\
\bottomrule
\end{tabular}
\caption{
Post-hoc ablations on the released test gold (RMSE$_{VA}$; lower is better), scored with the official evaluation script.
OOF calibration is fitted on Track~B training predictions only and then applied to test predictions.
}
\label{tab:ablation_loss}
\end{table*}

\subsection{Loss and Calibration Ablations (Post-hoc)}
\label{sec:ablation_loss_calib}

Using released test gold, we run controlled post-hoc experiments while varying (i) the regression loss (MSE vs.\ logistic-loss regression) and
(ii) whether OOF linear calibration is applied 
\citep{kuleshov2018accurate,guo2017calibration}.
Table~\ref{tab:ablation_loss} shows that regression loss choice substantially affects stability and cross-lingual robustness.


\subsection{Distribution Shift Analysis}
\label{sec:dist_shift}

To better understand the performance gap across languages, we analyze prediction behavior across languages.
Figure~\ref{fig:swa_zho_compare} visualizes gold versus predicted VA scores for ZHO and SWA on the released test gold.

As shown in Figure~\ref{fig:swa_zho_compare}, predictions for ZHO closely follow the diagonal reference line ($y=x$), indicating good calibration and dispersion.
In contrast, SWA predictions collapse into a narrow range around the mean, especially for arousal.
The fitted regression line becomes nearly flat and the correlation is close to zero, suggesting severe variance shrinkage under cross-lingual distribution shift.

\begin{figure}[ht]
    \centering
    \includegraphics[width=\columnwidth]{figures/fig_swa_vs_zho_compare.pdf}

    \caption{
    Gold vs.\ predicted scores for ZHO and SWA on the released test gold.
    The dashed line denotes the identity line ($y=x$), and the solid line shows a least-squares linear fit.
    Predictions for ZHO are well aligned with the gold scores, whereas SWA exhibits pronounced variance shrinkage, particularly for arousal.
    }

    \label{fig:swa_zho_compare}
\end{figure}
\FloatBarrier
Our model achieves the strongest performance on ZHO among the five languages.
One possible explanation is that the ZHO subset exhibits a relatively compact VA distribution, with most instances concentrated in a narrower middle region of the score space.
Such a distribution may make the regression mapping easier to learn.
Moreover, ZHO benefits from same-language Track~A$\rightarrow$Track~B transfer, which provides additional in-language supervision.

By contrast, SWA lacks such auxiliary supervision and shows substantially weaker generalization.
This observation is consistent with the dataset-level analysis in the DimStance paper.
Their Figure~2 shows that although all languages broadly follow a U-shaped valence--arousal relationship, the spread and shape of the VA space vary considerably across languages and domains \citep{becker2026dimstancemultilingualdatasetsdimensional}.
Taken together, these results suggest that under lower-resource conditions and stronger distribution shift, the model tends to produce under-dispersed predictions and loses sensitivity to arousal variation.


\section{Conclusion}
We presented NTNU-SMIL's system for SemEval-2026 Task~3 Track~B (DimStance) Subtask~1 (DimASR) \citep{yu-etal-2026-semeval,becker2026dimstancemultilingualdatasetsdimensional}.
Our approach integrates sentence-pair aspect conditioning, a final-layer \texttt{[CLS]} representation, dual VA heads, logistic-loss regression, same-language Track~A$\rightarrow$Track~B transfer, and lightweight calibration-aware ensembling.

Results and post-hoc analyses show that same-language cross-domain transfer and logistic-loss regression are the primary drivers of robust VA prediction when additional supervision is available.

Loss formulation has a stronger impact on stability than post-hoc calibration, while distribution-shift analysis reveals arousal variance collapse as a key failure mode in low-resource settings (SWA).

Future work will explore variance-preserving objectives and distribution-robust calibration strategies to mitigate cross-lingual degradation.
\section*{Limitations}
Our system relies on Track~A DimABSA data for same-language pretraining, which is available only for English and Chinese \citep{lee2026dimabsabuildingmultilingualandmultidomaindatasetsfordimensionalaspectbasedsentimentanalysis}.
Arousal prediction also remains vulnerable to variance shrinkage in lower-resource languages.

\section*{Acknowledgments}
This work was conducted at the Speech and Machine Intelligence Laboratory (SMIL), National Taiwan Normal University.
We thank the SemEval-2026 Task~3 organizers and SMIL members for their support and discussions.

\FloatBarrier
\clearpage
\bibliography{custom}

% Ensure appendix floats don't get "lost" before document end
\FloatBarrier
\clearpage

\appendix
\section{Appendix}
\label{sec:appendix}

% =========================
% FIX 2: add a subsection title for abbreviations
% =========================
\subsection{Abbreviations}
\label{sec:appendix_abbr}

Table~\ref{tab:abbr} summarizes abbreviations used in the paper.

\begin{table}[ht]
\centering
\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{
p{0.35\columnwidth}
p{0.25\columnwidth}
p{0.30\columnwidth}
}
\toprule
\textbf{Notation} & \textbf{Abbrev.} & \textbf{Meaning} \\
\midrule
Language ID (\textbf{Lang}) & ENG / DEU / ZHO / PCM / SWA &
English / German / Chinese / Nigerian Pidgin / Swahili \\

Domain (\textbf{Dom}) & Env.\ / Pol.\ &
Environmental Protection / Politics \\
\bottomrule
\end{tabular}
\caption{
Abbreviations used throughout the paper.
\textbf{Lang} denotes language identifiers, and \textbf{Dom} denotes domains in Track~B Subtask~1.
}
\label{tab:abbr}
\end{table}

\subsection{Pearson Correlation Coefficient}
\label{sec:appendix_metrics}

In addition to RMSE$_{VA}$, we report Pearson correlation coefficients (PCC) for valence and arousal:
\begin{equation}
\mathrm{PCC}_{V}
=
\mathrm{corr}\!\left(\{V_{p}^{(i)}\}_{i=1}^{N}, \{V_{g}^{(i)}\}_{i=1}^{N}\right),
\end{equation}
\begin{equation}
\mathrm{PCC}_{A}
=
\mathrm{corr}\!\left(\{A_{p}^{(i)}\}_{i=1}^{N}, \{A_{g}^{(i)}\}_{i=1}^{N}\right).
\end{equation}

\subsection{Training and Evaluation Protocol}
\label{sec:appendix_protocol}

\begin{table}[ht]
\centering
\small
\begin{tabular}{l l}
\toprule
\textbf{Stage} & \textbf{Data Usage} \\
\midrule
Stage 1 & Track~A train (ENG/ZHO only) \\
Stage 2 Train & Track~B train (5-fold group-based CV) \\
Calibration & OOF predictions on Track~B train \\
Model Selection & Track~B dev RMSE \\
Final Evaluation & Official hidden test set \\
\bottomrule
\end{tabular}
\caption{Training and evaluation protocol.}
\label{tab:appendix_protocol}
\end{table}

\subsection{Backbone Models}
\label{sec:appendix_backbones}

\begin{table}[ht]
\centering
\small
\setlength{\tabcolsep}{5pt}
\begin{tabular}{l l l}
\toprule
\textbf{Lang} & \textbf{Dom} & \textbf{Backbone (Hugging Face)} \\
\midrule
ENG & Env. & \texttt{microsoft/deberta-v3-large} \\
DEU & Pol. & \texttt{deepset/gbert-large} \\
ZHO & Env. & \texttt{hfl/chinese-roberta-wwm-ext-large} \\
PCM & Pol. & \texttt{Davlan/afro-xlmr-large} \\
SWA & Pol. & \texttt{Davlan/afro-xlmr-large} \\
\bottomrule
\end{tabular}
\caption{Language--domain specific backbone models used in our system.}
\label{tab:appendix_backbones}
\end{table}

\newpage
\subsection{Hyperparameter Settings}
\label{sec:appendix_hparams}

\begin{table}[ht]
\centering
\small
\setlength{\tabcolsep}{6pt}
\begin{tabular}{l l}
\toprule
\textbf{Hyperparameter} & \textbf{Value} \\
\midrule
Random seeds & \{42, 52, 62, 72, 82\} \\
\# CV folds ($K$) & 5 \\
Early stopping patience & 3 \\
Batch size & 8 \\
Gradient accumulation steps & 4 \\
Learning rate & $1.5 \times 10^{-5}$ \\
Weight decay & 0.01 \\
Dropout & 0.0 \\
Max epochs & 20 \\
Fixed folds & Yes \\
\bottomrule
\end{tabular}
\caption{Hyperparameter settings used in our experiments.}
\label{tab:hparams}
\end{table}

\end{document}