Challenge: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterrances in unannotated conversations.
Approach: They propose a new method to identify emotion utterances and their corresponding cause utterrances in unannotated conversations by using a center event-aware graph.
Outcome: The proposed model outperforms existing methods and achieves state-of-the-art performance across three benchmark datasets.

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Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment (2026.findings-acl)

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Challenge: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue.
Approach: They propose a framework for Emotion-Cause Pair Extraction in Conversations that decouples emotion-oriented semantics from cause-oriented ones and employs optimal transport to enable many-to-many and globally consistent emotion-cause matching.
Outcome: The proposed framework achieves state-of-the-art on several benchmark datasets.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)

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Challenge: Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously.
Approach: They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs.
Outcome: The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus .
Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts (2023.eacl-main)

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Challenge: Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents.
Approach: They propose a new Emotion-Cause Pair Extraction task in dialogue . they employ a ECPE dataset with more emotion-cause pairs in documents than news articles .
Outcome: The proposed model improves on a new english dialogue dataset with more emotion-cause pairs than news articles.
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)

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Challenge: Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text.
Approach: They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document.
Outcome: The proposed task is based on a benchmark emotion cause corpus.
Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction (2022.emnlp-main)

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Challenge: Emotion cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses.
Approach: They propose a novel task called emotion-cause pair extraction to extract emotion clauses and corresponding cause clauses.
Outcome: The proposed task can extract emotion clauses and cause clauses, and achieve state-of-the-art performance on the Chinese benchmark corpus.
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020.emnlp-main)

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Challenge: Existing methods to extract potential pairs of emotions ignore the fact that the cause and the emotion it triggers are inseparable.
Approach: They propose two frameworks that combine multi-label learning and multi-labeled learning to extract emotion clauses . they evaluate a benchmark emotion cause corpus and find the best performance .
Outcome: The proposed frameworks achieve the best performance among all compared systems on the ECPE task.
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)

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Challenge: Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors .
Approach: They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished.
Outcome: The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion.
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)

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Challenge: a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs .
Approach: They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme.
Outcome: The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws .
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction (2022.coling-1)

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Challenge: Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-caused pairs from an emotional document.
Approach: They propose a document-level machine reading comprehension task to model complex relations between emotions and causes while avoiding generating the pairing matrix.
Outcome: The proposed framework outperforms existing state-of-the-art methods on the emotion cause corpus and can model complex relations between emotions and causes while avoiding pairing matrix.
A Symmetric Local Search Network for Emotion-Cause Pair Extraction (2020.coling-main)

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Challenge: Existing methods for Emotion-cause pair extraction are not effective because of their lack of annotation.
Approach: They propose a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document.
Outcome: The proposed method outperforms existing state-of-the-art methods on the ECPE corpus.

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