Multi-turn Inference Matching Network for Natural Language Inference

摘要：

　　Natural Language Inference(NLI)is a fundamental and challenging task in Natural Language Processing(NLP).Most existing methods only apply one-pass inference process on a mixed matching feature,which is a concatenation of different matching features between a premise and a hypothesis.In this paper,we propose a new model called Multiturn Inference Matching Network(MIMN)to perform multi-turn inference on different matching features.In each turn,the model focuses on one particular matching feature instead of the mixed matching feature.To enhance the interaction between different matching features,a memory component is employed to store the history inference information.The inference of each turn is performed on the current matching feature and the memory.We conduct experiments on three different NLI datasets.The experimental results show that our model outperforms or achieves the state-of-the-art performance on all the three datasets.

关键词： Natural Language Inference Multi-turn inference Memory mechanism

作者: Chunhua Liu Shan Jiang Hainan Yu Dong Yu

作者单位: Beijing Language and Culture University,Beijing,China Beijing Language and Culture University,Beijing,China;Beijing Advanced Innovation for Language Resou

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 131-143

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）