Natural Language Inference Using LSTM Model with Sentence Fusion
作者单位：College of Electrical EngineeringZhejiang University
会议名称：《第36届中国控制会议》
会议届次：36
主办单位：Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
会议日期：2017年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Natural Language Inference Long Short-Term Memory Recognizing Textual Entailment Deep Learning
摘 要：Natural language inference（NLI） is a challenge and the foundation of realization of artificial *** the availability of large-scale annotated corpus,the neural network model can be widely used in natural language *** this paper,we propose a long short-term memory（LSTM） model with Sentence Fusion architecture for NLI *** of modifying the internal structure of the LSTM recurrent neural network（RNN） model,we focused on how to make full use of the distributed expression of sentence generated by the LSTM *** improved the performance of basic LSTM recurrent neural networks on Stanford natural language inference（SNLI） corpus by adding Sentence Fusion modules which enrich the distributed expression of sentence generated by the *** results demonstrate that the LSTM with Sentence Fusion which reads premise and hypothesis to produce a final fusion representation from which a three-way classifier predicts label has a better performance than LSTM RNN encoders and Lexicalized classifier.