Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)
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| Challenge: | Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios. |
| Approach: | They propose a novel task to find emotions and corresponding causes in unannotated emotion texts. |
| Outcome: | The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure. |
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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 . |
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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. |
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| Challenge: | Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text. |
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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. |
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| Challenge: | Emotion cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses. |
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| Challenge: | Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously. |
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| Challenge: | Existing methods for multi-label emotion classification are based on binary relevance and classifier chain (CC) |
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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. |
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