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Emotion-Cause Pair Extraction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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ECPE

ECPE-FanSplit

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Use these libraries to find emotion-cause-pair-extraction-5 models and implementations

Datasets

Xia and Ding, 2019

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Most implemented papers

Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts

Rui Xia, Zixiang Ding•Mon Jun 03 2019

A 2-step approach is proposed to address this new ECPE task, which first performs individual emotion extraction and cause extraction via multi-task learning, and then conduct emotion-cause pairing and filtering.

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End-to-end Emotion-Cause Pair Extraction via Learning to Link

Haolin Song, Chen Zhang, Qiuchi Li, D. Song•Mon Feb 24 2020

This work proposes a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner and outperforms a range of state-of-the-art approaches in terms of both effectiveness and efficiency.

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ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction

Zixiang Ding, Jianfei Yu, Rui Xia•Tue Jun 30 2020

A new end-to-end approach, called ECPE-Two-Dimensional (ECPE-2D), to represent the emotion-cause pairs by a 2D representation scheme and improves the F1 score of the state-of-the-art from 61.28% to 68.89%.

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Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction

Penghui Wei, Jiahao Zhao, W. Mao•Tue Jun 30 2020

This work tackles emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and proposes a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction.

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Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction

Lin Gui, Ruifeng Xu, Min Yang, Chuang Fan, Chaofa Yuan, Jiachen Du•Tue Jun 30 2020

A transition-based model is proposed to transform the emotion-cause pair extraction task into a procedure of parsing-like directed graph construction, from which it can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently.

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A Symmetric Local Search Network for Emotion-Cause Pair Extraction

Yafeng Yin, Zhiwei Jiang, Zifeng Cheng, Hua Yu, Qing Gu•Mon Nov 30 2020

A Symmetric Local Search Network model is proposed to perform the detection and matching simultaneously by local search of emotion and cause clauses in a document to demonstrate the superiority of this model over existing state-of-the-art methods.

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An End-to-End Network for Emotion-Cause Pair Extraction

Ashutosh Modi, Shreeshail Hingane, Saim Wani•Mon Mar 01 2021

This paper adapts the NTCIR-13 ECE corpus and establishes a baseline for the ECPE task to produce significant performance improvements over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.

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End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, Xiaoqiang Zhang•Mon Nov 30 2020

A novel end-to-end Pair Graph Convolutional Network (PairGCN) is presented to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs.

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A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness

Qixuan Sun, Yaqi Yin, Hong Yu•Wed Apr 14 2021

A Dual-Questioning Attention Network is proposed to alleviate limitations in emotion cause extraction by question candidate emotions and causes to the context independently through attention networks for a contextual and semantical answer.

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Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction

Hao Fei, Shengqiong Wu, Jingye Li, Fei Li, Donghong Ji, Shunjie Chen, Xiaochuan Shi•Thu Sep 08 2022

This work proposes a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature in between, and an inter- task alignment, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency.

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