@inproceedings{kazakov-etal-2024-petkaz,
title = "{P}et{K}az at {S}em{E}val-2024 Task 3: Advancing Emotion Classification with an {LLM} for Emotion-Cause Pair Extraction in Conversations",
author = "Kazakov, Roman and
Petukhova, Kseniia and
Kochmar, Ekaterina",
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-1.164/",
doi = "10.18653/v1/2024.semeval-1.164",
pages = "1127--1134",
abstract = "In this paper, we present our submission to the SemEval-2023 Task 3 {\textquotedblleft}The Competition of Multimodal Emotion Cause Analysis in Conversations{\textquotedblright}, focusing on extracting emotion-cause pairs from dialogs. Specifically, our approach relies on combining fine-tuned GPT-3.5 for emotion classification and using 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."
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%0 Conference Proceedings
%T PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
%A Kazakov, Roman
%A Petukhova, Kseniia
%A Kochmar, Ekaterina
%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 kazakov-etal-2024-petkaz
%X 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 using 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.
%R 10.18653/v1/2024.semeval-1.164
%U https://aclanthology.org/2024.semeval-1.164/
%U https://doi.org/10.18653/v1/2024.semeval-1.164
%P 1127-1134
Markdown (Informal)
[PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations](https://aclanthology.org/2024.semeval-1.164/) (Kazakov et al., SemEval 2024)
ACL