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.

本研究提出一种新型的模型，使用指针网络和统一依存 - 观点图来解决统一观点角色标注中的复杂性和限制性问题，并在 MPQA 基准测试上实现了最新成果。