End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, Xiaoqiang Zhang
Abstract
Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
BibTeX
@inproceedings{chen-etal-2020-end,
title = "End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network",
author = "Chen, Ying and
Hou, Wenjun and
Li, Shoushan and
Wu, Caicong and
Zhang, Xiaoqiang",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.17/",
doi = "10.18653/v1/2020.coling-main.17",
pages = "198--207"
}