TY - GEN
T1 - AIMA at SemEval-2024 Task 10
T2 - 18th International Workshop on Semantic Evaluation, SemEval 2024, co-located with the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL 2024
AU - Abootorabi, Mohammad Mahdi
AU - Ghazizadeh, Nona
AU - Dalili, Seyed Arshan
AU - Kure, Alireza Ghahramani
AU - Dehghani, Mahshid
AU - Asgari, Ehsaneddin
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024/7
Y1 - 2024/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85215511116&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85215511116
T3 - SemEval 2024 - 18th International Workshop on Semantic Evaluation, Proceedings of the Workshop
SP - 1704
EP - 1710
BT - SemEval 2024 - 18th International Workshop on Semantic Evaluation, Proceedings of the Workshop
A2 - Ojha, Atul Kr.
A2 - Dohruoz, A. Seza
A2 - Madabushi, Harish Tayyar
A2 - Da San Martino, Giovanni
A2 - Rosenthal, Sara
A2 - Rosa, Aiala
PB - Association for Computational Linguistics (ACL)
Y2 - 20 June 2024 through 21 June 2024
ER -