@inproceedings{tareh-etal-2024-iasbs,
title = "{IASBS} at {S}em{E}val-2024 Task 10: Delving into Emotion Discovery and Reasoning in Code-Mixed Conversations",
author = "Tareh, Mehrzad and
Mohandesi, Aydin and
Ansari, Ebrahim",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.179",
doi = "10.18653/v1/2024.semeval-1.179",
pages = "1229--1238",
abstract = "In this paper, we detail the IASBS team{'}s approach and findings from participating in SemEval-2024 Task 10, {``}Emotion Discovery and Reasoning in Hindi-English Code-mixed Conversations (EDiReF).{''} This task encompasses three critical subtasks: Emotion Recognition in Conversation (ERC), and Emotion Flip Reasoning (EFR) in both Hindi-English code-mixed and English dialogues. Our methodology integrates advanced NLP and machine learning techniques, focusing on the unique challenges of code-mixing, such as linguistic diversity and shifts in emotional context. By implementing a robust framework that includes data preprocessing, and feature engineering using models like GPT-4 and DistilBERT, we extend our analysis beyond mere emotion identification to explore the triggers behind emotion flips. This endeavor not only achieved third place on the leaderboard, demonstrating a high proficiency in emotion and flip detection with an F1-Score of 0.70 but also significantly contributed to the advancement of emotional AI. Our findings offer valuable insights into the complex interplay of emotions in communication, showcasing the potential for enhancing applications across various domains, from social media analytics to healthcare, and underscore the importance of understanding emotional dynamics in code-mixed conversations for future research and practical applications.",
}
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<abstract>In this paper, we detail the IASBS team’s approach and findings from participating in SemEval-2024 Task 10, “Emotion Discovery and Reasoning in Hindi-English Code-mixed Conversations (EDiReF).” This task encompasses three critical subtasks: Emotion Recognition in Conversation (ERC), and Emotion Flip Reasoning (EFR) in both Hindi-English code-mixed and English dialogues. Our methodology integrates advanced NLP and machine learning techniques, focusing on the unique challenges of code-mixing, such as linguistic diversity and shifts in emotional context. By implementing a robust framework that includes data preprocessing, and feature engineering using models like GPT-4 and DistilBERT, we extend our analysis beyond mere emotion identification to explore the triggers behind emotion flips. This endeavor not only achieved third place on the leaderboard, demonstrating a high proficiency in emotion and flip detection with an F1-Score of 0.70 but also significantly contributed to the advancement of emotional AI. Our findings offer valuable insights into the complex interplay of emotions in communication, showcasing the potential for enhancing applications across various domains, from social media analytics to healthcare, and underscore the importance of understanding emotional dynamics in code-mixed conversations for future research and practical applications.</abstract>
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%0 Conference Proceedings
%T IASBS at SemEval-2024 Task 10: Delving into Emotion Discovery and Reasoning in Code-Mixed Conversations
%A Tareh, Mehrzad
%A Mohandesi, Aydin
%A Ansari, Ebrahim
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F tareh-etal-2024-iasbs
%X In this paper, we detail the IASBS team’s approach and findings from participating in SemEval-2024 Task 10, “Emotion Discovery and Reasoning in Hindi-English Code-mixed Conversations (EDiReF).” This task encompasses three critical subtasks: Emotion Recognition in Conversation (ERC), and Emotion Flip Reasoning (EFR) in both Hindi-English code-mixed and English dialogues. Our methodology integrates advanced NLP and machine learning techniques, focusing on the unique challenges of code-mixing, such as linguistic diversity and shifts in emotional context. By implementing a robust framework that includes data preprocessing, and feature engineering using models like GPT-4 and DistilBERT, we extend our analysis beyond mere emotion identification to explore the triggers behind emotion flips. This endeavor not only achieved third place on the leaderboard, demonstrating a high proficiency in emotion and flip detection with an F1-Score of 0.70 but also significantly contributed to the advancement of emotional AI. Our findings offer valuable insights into the complex interplay of emotions in communication, showcasing the potential for enhancing applications across various domains, from social media analytics to healthcare, and underscore the importance of understanding emotional dynamics in code-mixed conversations for future research and practical applications.
%R 10.18653/v1/2024.semeval-1.179
%U https://aclanthology.org/2024.semeval-1.179
%U https://doi.org/10.18653/v1/2024.semeval-1.179
%P 1229-1238
Markdown (Informal)
[IASBS at SemEval-2024 Task 10: Delving into Emotion Discovery and Reasoning in Code-Mixed Conversations](https://aclanthology.org/2024.semeval-1.179) (Tareh et al., SemEval 2024)
ACL