论文标题
端到端中国语义角色标签的高级炼油
High-order Refining for End-to-end Chinese Semantic Role Labeling
论文作者
论文摘要
当前的端到端语义角色标记主要是通过基于图的神经模型来完成的。但是，所有这些都是一阶模型，其中每个检测任何谓词题目对的决定都是与本地特征隔离做出的。在本文中，我们提出了一种高阶精炼机制，以在所有谓词对题材对之间进行相互作用。基于基线图模型，我们的高级精炼模块通过注意计算在所有候选对之间学习高阶特征，后来用于更新原始令牌表示。经过几次改进后，基础令牌表示可以具有全球相互作用的特征。我们的高阶模型在包括Conll09和Universal命题银行在内的中国SRL数据上实现了最新的结果，同时缓解了长期依赖性问题。
Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.
