@inproceedings{zhang-etal-2024-samsung,
title = "{S}amsung Research {C}hina-{B}eijing at {S}em{E}val-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations",
author = "Zhang, Shen and
Zhang, Haojie and
Zhang, Jing and
Zhang, Xudong and
Zhuang, Yimeng and
Wu, Jinting",
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-1.81",
doi = "10.18653/v1/2024.semeval-1.81",
pages = "536--546",
abstract = "In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, LLaMA2-based InstructERC is utilized to extract the emotion category of each utterance in a conversation. After emotion recognition, a two-stream attention model is employed to extract the emotion causal pairs given the target emotion for subtask 2 while MuTEC is employed to extract causal span for subtask 1. Our approach achieved first place for both of the two subtasks in the competition.",
}
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<abstract>In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, LLaMA2-based InstructERC is utilized to extract the emotion category of each utterance in a conversation. After emotion recognition, a two-stream attention model is employed to extract the emotion causal pairs given the target emotion for subtask 2 while MuTEC is employed to extract causal span for subtask 1. Our approach achieved first place for both of the two subtasks in the competition.</abstract>
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%0 Conference Proceedings
%T Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
%A Zhang, Shen
%A Zhang, Haojie
%A Zhang, Jing
%A Zhang, Xudong
%A Zhuang, Yimeng
%A Wu, Jinting
%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 zhang-etal-2024-samsung
%X In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, LLaMA2-based InstructERC is utilized to extract the emotion category of each utterance in a conversation. After emotion recognition, a two-stream attention model is employed to extract the emotion causal pairs given the target emotion for subtask 2 while MuTEC is employed to extract causal span for subtask 1. Our approach achieved first place for both of the two subtasks in the competition.
%R 10.18653/v1/2024.semeval-1.81
%U https://aclanthology.org/2024.semeval-1.81
%U https://doi.org/10.18653/v1/2024.semeval-1.81
%P 536-546
Markdown (Informal)
[Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations](https://aclanthology.org/2024.semeval-1.81) (Zhang et al., SemEval 2024)
ACL