基于扩展的S-LSTM的文本蕴含识别

Extended S-LSTM Based Textual Entailment Recognition

文本蕴含识别旨在自动判断给定的前提和假设(通常为2个句子)之间是否存在蕴含关系,是自然语言处理领域一项基础但富有挑战的任务.当前,主流的基于深度学习的模型通常分别建模前提和假设的语义表示,而没有把它们看作一个整体;另外,在捕获它们之间的语义关系时,大都没有同时利用句子级别的全局信息和短语级别的局部信息.最近提出的S-LSTM能够同时学习句子和短语的语义表示,在文本分类等任务上取得了较好的效果.基于上述情况,提出了一种基于扩展的S-LSTM的文本蕴含识别模型.一方面,把前提和假设看作一个整体,扩展S-LSTM以同时学习它们的语义表示;另一方面,在建模语义关系时,既利用句子级别的信息又利用短语级别的信息,以此获得更好的语义表示.在英文SNLI数据集和中文CNLI数据集上的实验结果表明:提出的模型取得了比基准模型更好的识别性能.

Text entailment recognition aims at automatically determining whether there is an entailment relationship between the given premise and hypothesis(usually two sentences).It is a basic and challenging task in natural language processing.Current dominant models,which are based on deep learning,usually encode the semantic representations of two sentences separately,instead of considering them as a whole.Besides,most of them do not leverage both the sentence-level global and ngram-level local information when capturing the semantic relationship.The recently proposed S-LSTM can learn semantic representations of a sentence and its ngrams simultaneously,achieving promising performance on tasks such as text classification.Considering the above,a model based on an extended S-LSTM is proposed for textual entailment recognition.On the one hand,S-LSTM is extended to learn semantic representations of the premise and hypothesis simultaneously,which regards them as a whole.On the other hand,to obtain better semantic representation,both the sentence-level and ngram-level information are used to capture the semantic relationships.Experimental results,on the English SNLI dataset and Chinese CNLI dataset,show that the performance of the proposed model is better than baselines.