Mastering the Explicit Opinion-Role Interaction: Syntax-Aided Neural Transition System for Unified Opinion Role Labeling

Authors

  • Shengqiong Wu Wuhan University
  • Hao Fei Wuhan University
  • Fei Li Wuhan University
  • Meishan Zhang Harbin Institute of Technology (Shenzhen), China
  • Yijiang Liu Wuhan University
  • Chong Teng Wuhan University
  • Donghong Ji Wuhan University

DOI:

https://doi.org/10.1609/aaai.v36i10.21404

Keywords:

Speech & Natural Language Processing (SNLP)

Abstract

Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by employing the span-based graph model, which however still suffers from both high model complexity and insufficient interaction among opinions and roles. In this work, we investigate a novel solution by revisiting the transition architecture, and augmenting it with a pointer network (PointNet). The framework parses out all opinion structures in linear-time complexity, meanwhile breaks through the limitation of any length of terms with PointNet. To achieve the explicit opinion-role interactions, we further propose a unified dependency-opinion graph (UDOG), co-modeling the syntactic dependency structure and the partial opinion-role structure. We then devise a relation-centered graph aggregator (RCGA) to encode the multi-relational UDOG, where the resulting high-order representations are used to promote the predictions in the vanilla transition system. Our model achieves new state-of-the-art results on the MPQA benchmark. Analyses further demonstrate the superiority of our methods on both efficacy and efficiency.

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Published

2022-06-28

How to Cite

Wu, S., Fei, H., Li, F., Zhang, M., Liu, Y., Teng, C., & Ji, D. (2022). Mastering the Explicit Opinion-Role Interaction: Syntax-Aided Neural Transition System for Unified Opinion Role Labeling. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 11513-11521. https://doi.org/10.1609/aaai.v36i10.21404

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing