论文标题
通过消息传递和端到端培训的二阶神经依赖性解析
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
论文作者
论文摘要
在本文中，我们使用消息传递和端到端神经网络提出了基于二阶的神经依赖性解析。我们从经验上表明，我们的方法与最近最先进的基于二阶基于图的神经依赖性解析器的准确性相匹配，并且在训练和测试中的速度明显更快。我们还从经验上显示了二阶解析比一阶解析的优势，并观察到使用BERT嵌入时头部选择结构化约束的有用性消失了。
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.
