An End-to-End Network for Emotion-Cause Pair Extraction
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.
Cite
@article{arxiv.2103.01544,
title = {An End-to-End Network for Emotion-Cause Pair Extraction},
author = {Aaditya Singh and Shreeshail Hingane and Saim Wani and Ashutosh Modi},
journal= {arXiv preprint arXiv:2103.01544},
year = {2021}
}
Comments
Accepted at WASSA-2021, 5 Pages + 2 Pages (references) + 2 Pages (Appendix)