Neural Transition System for End-to-End Opinion Role Labeling
- URL: http://arxiv.org/abs/2110.02001v1
- Date: Tue, 5 Oct 2021 12:45:59 GMT
- Title: Neural Transition System for End-to-End Opinion Role Labeling
- Authors: Shengqiong Wu and Donghong Ji
- Abstract summary: Unified opinion role labeling (ORL) aims to detect all possible opinion structures of opinion-holder-target' in one shot, given a text.
We propose a novel solution by revisiting the transition architecture, and augment it with a pointer network (PointNet)
The framework parses out all opinion structures in linear-time complexity, breaks through the limitation of any length of terms with PointNet.
- Score: 13.444895891262844
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 augment 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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