Yiyang Chen


2024

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UIR-ISC at SemEval-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations
Hongyu Guo | Xueyao Zhang | Yiyang Chen | Lin Deng | Binyang Li
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.