An End-to-End Network for Emotion-Cause Pair Extraction
Published in
Workshop on Computational Approaches to Subject...(2021)External Links:
TL
TL;DR
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.
Abstract
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (∼ 6.5% increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.
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A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
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