2025/01/19 by Mohammad Mahdi Abootorabi, Abootorabi, Mohammad Mahdi, Nona Ghazizadeh +9
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Emotion and Mood Recognition #FOS: Computer and information sciences #Language, Metaphor, and Cognition #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.2501.11166
openalex publication_date 2025/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.