research-article

A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction

Authors:

Jie LiuAuthors Info & Claims

ACAI '20: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 63, Pages 1 - 9

https://doi.org/10.1145/3446132.3446195

Published: 09 March 2021 Publication History

Abstract

Unlike Emotion Cause Extraction (ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction (ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network (IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus.

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Cited By

Chen ZZhang MPalade VWang LZhang JFeng Y(2024)An Emotion Type Informed Multi-Task Model for Emotion Cause Pair ExtractionIEEE Access10.1109/ACCESS.2024.335798212(15662-15674)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3357982
Yu JLiu WHe YZhong B(2022)A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair ExtractionElectronics10.3390/electronics1118288411:18(2884)Online publication date: 12-Sep-2022
https://doi.org/10.3390/electronics11182884
Wang BMa TLu ZXu H(2022)An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft SharingApplied Sciences10.3390/app1218899812:18(8998)Online publication date: 7-Sep-2022
https://doi.org/10.3390/app12188998

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ACAI '20: Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

December 2020

576 pages

ISBN:9781450388115

DOI:10.1145/3446132

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 09 March 2021

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the National Key Research and Development Program of China
the National Key Research and Development Program of China,

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ACAI 2020

ACAI 2020: 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence

December 24 - 26, 2020

Sanya, China

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Overall Acceptance Rate 173 of 395 submissions, 44%

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Cited By

Chen ZZhang MPalade VWang LZhang JFeng Y(2024)An Emotion Type Informed Multi-Task Model for Emotion Cause Pair ExtractionIEEE Access10.1109/ACCESS.2024.335798212(15662-15674)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3357982
Yu JLiu WHe YZhong B(2022)A Hierarchical Heterogeneous Graph Attention Network for Emotion-Cause Pair ExtractionElectronics10.3390/electronics1118288411:18(2884)Online publication date: 12-Sep-2022
https://doi.org/10.3390/electronics11182884
Wang BMa TLu ZXu H(2022)An End-to-End Mutually Interactive Emotion–Cause Pair Extractor via Soft SharingApplied Sciences10.3390/app1218899812:18(8998)Online publication date: 7-Sep-2022
https://doi.org/10.3390/app12188998
Fan CLi DWu Y(2022)Combining BERT with Bi-LSTM for Emotion-Cause Pair Extraction2022 4th International Conference on Computer Communication and the Internet (ICCCI)10.1109/ICCCI55554.2022.9850274(1-6)Online publication date: 1-Jul-2022
https://doi.org/10.1109/ICCCI55554.2022.9850274

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