Papers › FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion...
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations
Zuhair Hasan Shaik, Dhivya Prasanna, Enduri Jahnavi, Rishi Thippireddy, Vamsi Madhav, Sunil Saumya, Shankar Biradar
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Emotion Recognition | MaSaC_ERC | MaSaC-ERC-Z | F1-score (Weighted) | 51.17 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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