基于多层局部推理的汉语篇章关系及主次联合识别

篇章关系识别是篇章分析的核心组成部分。汉语中,缺少显式连接词的隐式篇章关系占比很高,篇章关系识别更具挑战性。该文给出了一个基于多层局部推理的汉语篇章关系及主次联合识别方法。该方法借助双向LSTM和多头自注意力机制进行篇章关系对应论元的表征;进一步借助软对齐方式获取论元间局部语义的推理权重,形成论元间交互语义信息的表征;再将两类信息结合进行篇章关系的局部推理,并通过堆叠多层局部推理部件构建了汉语篇章关系及主次联合识别框架,在CDTB语料库上的关系识别F₁值达到了67.0%。该文进一步将该联合识别模块嵌入一个基于转移的篇章解析器,在自动生成的篇章结构下进行篇章关系及主次的联合分析,形成了完整的汉语篇章解析器。

Multi-layer Local Inference Based Chinese Discourse Relation and Nuclearity Recognition

Discourse relation recognition plays a crucial part in discourse parsing. In Chinese, the task is much more challenging due to the high proportion of implicit discourse relations without explicit connectives as inference clues. This paper proposed a multi-layer local inference method for Chinese Discourse Relation Recognition. It employs bi-directional LSTM and multi-head self-attention mechanism to encode independent arguments, and then generate interactive pair representations using soft alignment between arguments achieved with soft attention. Both independent representations and interactive representations are then combined to perform local inference. By stacking the above local inference modules in our framework, we achieve 67.0% in Macro-F₁ value on CDTB corpus. Furthermore, a full automatic discourse parser is established by incorporating our trained model into an existing transition-based Chinese discourse parser, which can jointly learn the discourse relation and nuclearity.