@inproceedings{shaik-etal-2024-feedforward,
title = "{F}eed{F}orward at {S}em{E}val-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations",
author = "Shaik, Zuhair Hasan and
Prasanna, Dhivya and
Jahnavi, Enduri and
Thippireddy, Rishi and
Madhav, Vamsi and
Saumya, Sunil and
Biradar, Shankar",
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-1.107/",
doi = "10.18653/v1/2024.semeval-1.107",
pages = "745--756",
abstract = "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."
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%0 Conference Proceedings
%T FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations
%A Shaik, Zuhair Hasan
%A Prasanna, Dhivya
%A Jahnavi, Enduri
%A Thippireddy, Rishi
%A Madhav, Vamsi
%A Saumya, Sunil
%A Biradar, Shankar
%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 shaik-etal-2024-feedforward
%X 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.
%R 10.18653/v1/2024.semeval-1.107
%U https://aclanthology.org/2024.semeval-1.107/
%U https://doi.org/10.18653/v1/2024.semeval-1.107
%P 745-756
Markdown (Informal)
[FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations](https://aclanthology.org/2024.semeval-1.107/) (Shaik et al., SemEval 2024)
ACL