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AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations

Abootorabi, M. M ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. Publisher: 2024
  3. Abstract:
  4. In this study, we introduce a solution to the SemEval 2024 Task 10 on subtask 1, dedicated to Emotion Recognition in Conversation (ERC) in code-mixed Hindi-English conversations. ERC in code-mixed conversations presents unique challenges, as existing models are typically trained on monolingual datasets and may not perform well on code-mixed data. To address this, we propose a series of models that incorporate both the previous and future context of the current utterance, as well as the sequential information of the conversation. To facilitate the processing of code-mixed data, we developed a Hinglish-to-English translation pipeline to translate the code-mixed conversations into English. We designed four different base models, each utilizing powerful pre-trained encoders to extract features from the input but with varying architectures. By ensembling all of these models, we developed a final model that outperforms all other baselines. © 2024 Association for Computational Linguistics
  5. Keywords:
  6. Pipeline codes ; Speech recognition ; Translation (languages) ; Base models ; Emotion recognition ; Mixed data ; Sequential information ; Subtask ; Computational linguistics
  7. Source: SemEval 2024 - 18th International Workshop on Semantic Evaluation, Proceedings of the Workshop ; 2024 , Pages 1704-1710 ; 979-889176107-0 (ISBN)
  8. URL: https://aclanthology.org/2024.semeval-1.244