Decker Krogh

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2024

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UCSC NLP at SemEval-2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)
Neng Wan | Steven Au | Esha Ubale | Decker Krogh
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

We describe SemEval-2024 Task 10: EDiReF consisting of three sub-tasks involving emotion in conversation across Hinglish code-mixed and English datasets. Subtasks include classification of speaker emotion in multiparty conversations (Emotion Recognition in Conversation) and reasoning around shifts in speaker emotion state (Emotion Flip Reasoning). We deployed a BERT model for emotion recognition and two GRU-based models for emotion flip. Our model achieved F1 scores of 0.45, 0.79, and 0.68 for subtasks 1, 2, and 3, respectively.
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