论文标题
通过指针网络基于过渡的语义依赖性解析
Transition-based Semantic Dependency Parsing with Pointer Networks
论文作者
论文摘要
通过指针网络实施的基于过渡的解析器已成为依赖性解析的新技术，在生产标记的句法树和在此任务中表现优于基于图的模型。为了进一步测试这些功能强大的神经网络在更严重的NLP问题上的功能，我们提出了一个过渡系统，借助指针网络，它可以直接生成标记为有针对性的无环图并执行语义依赖性解析。此外，我们通过从Bert提取的深层上下文化的单词嵌入来增强我们的方法。最终的系统不仅胜过所有现有的基于过渡的模型，而且还匹配了迄今为止在Semeval 2015 Task 18中的最佳完全监督的精度，在先前的基于图形的最先进的基于图的解析器中，英语数据集。
Transition-based parsers implemented with Pointer Networks have become the new state of the art in dependency parsing, excelling in producing labelled syntactic trees and outperforming graph-based models in this task. In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing. In addition, we enhance our approach with deep contextualized word embeddings extracted from BERT. The resulting system not only outperforms all existing transition-based models, but also matches the best fully-supervised accuracy to date on the SemEval 2015 Task 18 English datasets among previous state-of-the-art graph-based parsers.
