基于深度学习的隐式篇章关系识别综述

隐式篇章关系识别是自然语言处理中一项富有挑战性的任务,旨在判断缺少连接词的两个论元(子句或者句子)之间的语义关系(例如转折)。近年来,随着深度学习在自然语言处理领域的广泛应用,各种基于深度学习的隐式篇章关系识别方法取得了不错的效果,其性能全面超越了早期基于人工特征的方法。文中分三大类对最近的隐式篇章关系识别方法进行讨论:基于论元编码的方法、基于论元交互的方法和引入显式篇章数据的半监督方法。在PDTB数据集上的实验结果显示:1)通过显式地建模论元中词或文本片段之间的语义关系,基于论元交互的方法的性能明显好于基于论元编码的方法;2)引入显式篇章数据的半监督方法能有效地缓解数据稀疏问题,从而进一步提升识别的性能。最后,分析了当前面临的主要问题,并指出了未来可能的研究方向。

Survey of Implicit Discourse Relation Recognition Based on Deep Learning

Implicit discourse relation recognition is still a challenging task in natural language processing.It aims to discover the semantic relations(such as transition)between two arguments(e.g.clauses or sentences)where discourse connectives are absent.In recent years,with the extensive application of deep learning in natural language processing,various methods based on deep learning have achieved promising results on implicit discourse relation recognition.Their performance is much better than that of previous methods based on manual features.This paper discussed recent implicit discourse recognition methods in three categories:argument encoding based methods,argument interaction based methods and semi-supervised methods with explicit discourse data.Results on the PDTB data set show that,by explicitly modeling the semantic relation between words or text spans in two arguments,the performance of argument interaction based methods is significantly better than that of argument encoding based methods,and by incorporating explicit discourse data,the semi-supervised methods can effectively alleviate the problem of data sparsity,and then further improve the recognition performance.Lastly,this paper analyzed the major problems faced at pre-sent,and pointed out the possible research directions.