TY - GEN
T1 - FeedForward 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 - Shaik, Zuhair Hasan
AU - Prasanna, R. Dhivya
AU - Jahnavi, Enduri
AU - Thippireddy, Rishi Koushik Reddy
AU - Madhav, P. S.S.Vamsi
AU - Saumya, Sunil
AU - Biradar, Shankar
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - This paper reports on an innovative approach to Emotion Recognition in Conversation and Emotion Flip Reasoning for the SemEval-2024 competition with a specific focus on analyzing Hindi-English code-mixed language. By integrating Large Language Models (LLMs) with Instruction-based Fine-tuning and Quantized Low-Rank Adaptation (QLoRA), this study introduces innovative techniques like Sentext-height and advanced prompting strategies to navigate the intricacies of emotional analysis in code-mixed conversational data. The results of the proposed work effectively demonstrate its ability to overcome label bias and the complexities of code-mixed languages. Our team achieved ranks of 5, 3, and 3 in tasks 1, 2, and 3 respectively. This study contributes valuable insights and methods for enhancing emotion recognition models, underscoring the importance of continuous research in this field.
AB - This paper reports on an innovative approach to Emotion Recognition in Conversation and Emotion Flip Reasoning for the SemEval-2024 competition with a specific focus on analyzing Hindi-English code-mixed language. By integrating Large Language Models (LLMs) with Instruction-based Fine-tuning and Quantized Low-Rank Adaptation (QLoRA), this study introduces innovative techniques like Sentext-height and advanced prompting strategies to navigate the intricacies of emotional analysis in code-mixed conversational data. The results of the proposed work effectively demonstrate its ability to overcome label bias and the complexities of code-mixed languages. Our team achieved ranks of 5, 3, and 3 in tasks 1, 2, and 3 respectively. This study contributes valuable insights and methods for enhancing emotion recognition models, underscoring the importance of continuous research in this field.
UR - https://www.scopus.com/pages/publications/85215499759
UR - https://www.scopus.com/pages/publications/85215499759#tab=citedBy
U2 - 10.18653/v1/2024.semeval-1.107
DO - 10.18653/v1/2024.semeval-1.107
M3 - Conference contribution
AN - SCOPUS:85215499759
T3 - SemEval 2024 - 18th International Workshop on Semantic Evaluation, Proceedings of the Workshop
SP - 745
EP - 756
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 -