基于多任务双向长短时记忆网络的隐式句间关系分析

隐式句间关系识别是篇章句间关系识别任务中一个重要的问题。由于隐式句间关系的语料没有较好的特征,目前该任务的识别仍不能达到很好的效果。隐式句间关系的语句和显式句间关系的语句在语义等方面有着一定的联系,为了充分利用这两个任务之间的联系,该论文使用多任务学习的方法,并使用双向长短时记忆(BiLSTM)网络学习语句的相关特征;同时,为充分利用文本的特征,采用融合词嵌入的方法并引入先验知识。与其他基于哈工大的中文篇章级语义关系语料库的实验结果表明,该文方法的平均F1值为53%,提升约13%;平均召回率(Recall)为51%,提升约9%。

Implicit Discourse Relation Analysis Based on Multi-task Bi-LSTM

Implicit discourse relation recognition is an important issue in the task of discourse relationship recognition. Nowadays, the corpus of implicit discourse relationship does not provide enough information for good results. To make full use of the fact that the sentences of implicit discourse and explicit discourse have some contact in semantic or some other aspects, this paper adopts multi-task learning method to handle the recognition task. The bidirectional long short term memory (Bi-LSTM) network is applied to learn the related features of the sentences. At the same time, the method of merging word vector has been adopted together with prior knowledge. Compared with other results, experiment results on the HIT-CDTB show that the average F₁ score of this paper reaches 53% (about 13% relative improvement), and the average recall score reaches 51% (about 9% relative improvement).