Span-level emotion cause analysis by BERT-based graph attention network

Publication Type

Conference Proceeding Article

Publication Date

11-2021

Abstract

We study the task of span-level emotion cause analysis (SECA), which is focused on identifying the specific emotion cause span(s) triggering a certain emotion in the text. Compared to the popular clause-level emotion cause analysis (CECA), it is a finer-grained emotion cause analysis (ECA) task. In this paper, we design a BERT-based graph attention network for emotion cause span(s) identification. The proposed model takes advantage of the structure of BERT to capture the relationship information between emotion and text, and utilizes graph attention network to model the structure information of the text. Our SECA method can be easily used for extracting clause-level emotion causes for CECA as well. Experimental results show that the proposed method consistently outperforms the state-of-the-art ECA methods on benchmark emotion cause dataset.

Discipline

Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Proceedings of the 30th ACM International Conference on Information and Knowledge Management

First Page

3221

Last Page

3226

Identifier

10.1145/3459637.3482185

Publisher

ACM

City or Country

New York, USA

Additional URL

http://doi.org/10.1145/3459637.3482185

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