| 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. |
Similar Papers
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 . |
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. |
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. |
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
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. |
Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning (2024.findings-emnlp)
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| 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. |
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. |