\subsection{Main Results}
\input{table/results}
Table~\ref{tab:main_result} reports the final performance of our system on the official test sets across all 10 language--domain pairs, in comparison with the official baselines.
Our system outperforms the strongest supervised fine-tuning baseline, GPT-OSS 120B, on 9 out of 10 language–domain pairs.
Moreover, it consistently surpasses both the one-shot prompted closed-source LLM Kimi K2 Thinking and the QLoRA fine-tuned Qwen3-14B across all pairs.

On the official leaderboard, our system ranks 1st on \textit{zho-res}, 2nd on \textit{zho-lap}, and 3rd on \textit{jpn-hot}, with all remaining datasets placed within the top half of participating teams. These results demonstrate that a lightweight PLM-based system, when equipped with joint multilingual training and adaptive ensemble selection, can be highly competitive against LLM-based approaches without relying on external corpora or data augmentation.

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\subsection{Ablation Study}
\input{table/ablation_study}
To validate the contribution of each component in our framework, we conduct a series of ablation experiments on the official development set. Results are reported in Table~\ref{tab:ablation}.


\paragraph{Joint Multilingual Training.}
Taking mBERT as a representative backbone, we observe that joint training significantly outperforms separate training across all pairs, yielding an overall average RMSE reduction of 10.9\%. 
Notably, the gains are most pronounced for low-resource language-domain pairs, such as \textit{ukr-res} (-21.3\%) and \textit{tat-res} (-19.9\%), whereas improvements are comparatively modest for higher-resource pairs such as \textit{eng-lap} (-1.2\%) and \textit{zho-lap} (-2.9\%). This asymmetric pattern suggests that pooling data across languages and domains provides implicit supervision for low-resource pairs, effectively alleviating data sparsity through shared cross-lingual representations.


\paragraph{Backbone Model Size.}
Block~2 compares three backbone encoders under joint training. Scaling from mBERT to XLM-R Large results in a consistent performance boost, demonstrating that the superior cross-lingual representation capability of XLM-R Large provides a more robust foundation for capturing the subtle nuances of valence and arousal dimensions.

\paragraph{Bounded Output Transformation.}
We examine the effect of the sigmoid-based bounded transformation (Equation \ref{eq:sigmoid}) by using a representative configuration (XLM-R Large, batch size 16, learning rate 1e-5), and observe an average RMSE reduction of 3.7\%. Although the gains are not uniform across all language--domain pairs, the bounded transformation generally stabilizes training and prevents out-of-range predictions. In the final ensemble, we include both bounded and unbounded models to balance robustness and flexibility, leveraging their complementary strengths.


\paragraph{Ensemble Strategy.}
Block 4 shows that the adaptive subset ensemble consistently outperforms the best single model across all 10 datasets (avg.\ $-$2.9\%), confirming that exhaustive subset search over the candidate pool effectively reduces prediction variance.