@inproceedings{guo-etal-2024-uir,
title = "{UIR}-{ISC} at {S}em{E}val-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations",
author = "Guo, Hongyu and
Zhang, Xueyao and
Chen, Yiyang and
Deng, Lin and
Li, Binyang",
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-1.110",
doi = "10.18653/v1/2024.semeval-1.110",
pages = "770--776",
abstract = "The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18{\%}, which further shows that our system has a robust performance.",
}
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<abstract>The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.</abstract>
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%0 Conference Proceedings
%T UIR-ISC at SemEval-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations
%A Guo, Hongyu
%A Zhang, Xueyao
%A Chen, Yiyang
%A Deng, Lin
%A Li, Binyang
%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 guo-etal-2024-uir
%X The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.
%R 10.18653/v1/2024.semeval-1.110
%U https://aclanthology.org/2024.semeval-1.110
%U https://doi.org/10.18653/v1/2024.semeval-1.110
%P 770-776
Markdown (Informal)
[UIR-ISC at SemEval-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations](https://aclanthology.org/2024.semeval-1.110) (Guo et al., SemEval 2024)
ACL