论文标题
通过指针网络基于过渡的语义角色标签
Transition-based Semantic Role Labeling with Pointer Networks
论文作者
论文摘要
语义角色标签（SRL）着重于识别句子的谓词题目结构，并在许多自然语言处理任务（例如机器翻译和问题回答）中起着至关重要的作用。实际上，所有可用的方法都不执行完整的SRL，因为它们依赖于预识别的谓词，并且其中大多数遵循管道策略，使用特定模型来执行一个或几个SRL子任务。此外，尽管句法树同样难以生产，但以前的方法对句法信息具有很大的依赖，以实现最新的性能。这些简化和要求使大多数SRL系统对于实际应用不切实际。在本文中，我们提出了第一种基于过渡的SRL方法，该方法能够在单个左右通行证中彻底处理输入句子，而既不利用句法信息也不诉诸其他模块。多亏了我们基于指针网络的实现，可以在$ O（n^2）$中准确，有效地完成完整的SRL，从而在Conll-2009共享任务中实现迄今为止大多数语言的最佳性能。
Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in $O(n^2)$, achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task.
