In this paper, we present our submission to the SemEval-2023 Task~3 "The Competition of Multimodal Emotion Cause Analysis in Conversations", focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and a BiLSTM-based neural network to detect causes. We score 2nd in the ranking for Subtask 1, demonstrating the effectiveness of our approach through one of the highest weighted-average proportional F1 scores recorded at 0.264.
@article{arxiv.2404.05502,
title = {PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations},
author = {Roman Kazakov and Kseniia Petukhova and Ekaterina Kochmar},
journal= {arXiv preprint arXiv:2404.05502},
year = {2024}
}
Comments
8 pages, 7 figures, 2 tables, to be published in the Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), for associated code, see https://github.com/sachertort/petkaz-semeval-ecac