Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks

Bo Zhang, Yue Zhang, Rui Wang, Zhenghua Li, Min Zhang


Abstract
Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer “who expressed what kind of sentiment towards what?”. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations. We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels. In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously. We verify our methods on the benchmark MPQA corpus. The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntax-agnostic baseline. In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT). Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.
Anthology ID:
2020.acl-main.297
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3249–3258
Language:
URL:
https://aclanthology.org/2020.acl-main.297
DOI:
10.18653/v1/2020.acl-main.297
Bibkey:
Cite (ACL):
Bo Zhang, Yue Zhang, Rui Wang, Zhenghua Li, and Min Zhang. 2020. Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3249–3258, Online. Association for Computational Linguistics.
Cite (Informal):
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks (Zhang et al., ACL 2020)
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PDF:
https://aclanthology.org/2020.acl-main.297.pdf
Software:
 2020.acl-main.297.Software.zip
Dataset:
 2020.acl-main.297.Dataset.pdf
Video:
 http://slideslive.com/38929056