YNU - HPCC at S em E val-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers (2025)

Abstract: This paper describes the our team’s participation in Subtask A of Task 11 at SemEval-2025, focusing on multilingual text-based emotion classification. The team employed the RoBERTa model, enhanced with modifications to the output head to allow independent prediction of six emotions: anger, disgust, fear, joy, sadness, and surprise. The dataset was translated into English using Google Translate to facilitate processing. The study found that a single prediction head outperformed simultaneous prediction of multiple emotions, and training on the translated dataset yielded better results than using the original dataset. The team incorporated Focal Loss and R-Drop techniques to address class imbalance and improve model stability. Future work will continue to explore improvements in this area.

## introduction multilingual sentiment classification is crucial in natural language processing (nlp), aiming to analyze emotional expressions across languages. this task is key for applications such as opinion mining, customer feedback analysis, and cross-cultural sentiment studies. it involves handling linguistic variations and challenges posed by low-resource languages, making it an important area of research. recent research has focused on multilingual sentiment classification, especially with large-scale multilingual datasets and benchmarks @xcite . approaches such as translating text into english and leveraging english embeddings have improved performance across languages @xcite . new annotation methods have been introduced at various levels (word, sentence, document) @xcite . for low-resource languages, methods that work with unlabeled parallel corpora have also been proposed @xcite . in this study, we examine several transformerbased models (bert, roberta, albert, distil-bert, electra, deberta, and mbert) and their language support. as shown in based on the experimental results, we chose roberta as our base model and fine-tuned it for six emotions: anger, disgust, fear, joy, sadness, and surprise. we incorporated r-drop and focal loss techniques to improve training, which led to the final results. ## related work sentiment analysis using recurrent neural networks (rnns) and machine translation has been explored in @xcite . this study investigates the feasibility and effectiveness of performing multilingual sentiment analysis through machine translation, particularly with the use of google translate. it reveals that the performance of machine translation in sentiment analysis diverges from that of human expert translations, especially regarding the accuracy of emotional expression and semantic similarity @xcite . this paper further explores the variations in sentiment analysis during the translation process, such as differences in emotional expression across languages and the impact of translation on sentiment polarity @xcite . by analyzing users' emotions in real time, ## methodology given the limitations of directly training a multilingual model, translating target language text into english and utilizing english sentiment analysis tools has proven effective for cross-lingual sentiment analysis. experimental results show that the elsa model significantly improved performance across multiple tasks @xcite . additionally, cross-lingual models have shown strong performance in sentiment detection, notably when leveraging translated english data and fine-tuned contextual embeddings @xcite . after translation, we used the deberta model for emotion classification. deberta, an advanced transformer-based model, improves upon bert and roberta with disentangled attention and absolute position embeddings, which enhance its ability to capture complex linguistic and contextual information. please refer to figure 1 for details on the method. ## experimentation to evaluate the effectiveness of our approach, we conducted a series of experiments. these experiin our setup, we modified the prediction head of the deberta model, enabling it to predict one emotion at a time and two emotions at once. the model was fine-tuned for emotion classification, predicting six distinct emotions: anger, disgust, fear, joy, sadness, and surprise. ## conclusions this study presents the ynu-hpcc team and the participation in semeval-2025 subtask a of task 11. we made predictions for 29 languages and used deberta as the baseline model. we modified the prediction head to allow for independent predictions in each instance. our proposed model demonstrated its effectiveness in addressing this task. among the various results we submitted, the combination of focal loss, r-drop, and deberta achieved the highest score of 0.44 in