掌握明确的意见-角色互动：用于统一意见角色标注的句法辅助神经转换系统
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling
摘要：
统一意见角色标注（ORL）的目的是检测所有可能的意见结构的“意见持有者目标”在一个镜头，给定的文本。现有的基于转换的统一方法，不幸的是，是受较长的意见条款，并未能解决术语重叠的问题。目前的最高性能已经实现了采用基于跨度的图模型，但仍然受到高模型复杂性和意见和角色之间的互动不足。在这项工作中，我们研究了一种新的解决方案，通过重新访问的过渡架构，并增加了一个指针网络（PointNet）。该框架以线性时间复杂度解析出所有的意见结构，同时突破了PointNet对任意词长的限制。为了实现明确的意见-角色的互动，我们进一步提出了一个统一的依赖意见图（UDOG），共同建模的句法依赖结构和部分意见-角色结构。然后，我们设计了一个以关系为中心的图聚合器（RCGA）编码的多关系UDOG，其中产生的高阶表示用于促进香草过渡系统中的预测。我们的模型在MPQA基准测试中取得了新的最先进的结果。分析进一步证明了我们的方法在有效性和效率上的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
