Abstract: The ultimate goal of artificial intelligence is to let machine understand human natural language in cognitive field.Machine reading comprehension raises great challenge in natural language processing which requires computer to have certain common knowledge,comprehensively understand text material,and correctly answer the corresponding questions according to that text material.With the rapid development of deep learning,machine reading comprehension becomes the current hotspot research direction in artificial intelligence,involving core technologies such as machine learning,information retrieval,semantic computing and has been widely used in chat robots,question answering systems and intelligent education.This paper focuses on micro-rea-ding mode,and answer candidate sentences containing answers are extracted from given text,which provide technology support for machine reading comprehension.Traditional feature-based methods consumes lots of manpower.This paper regards candidate sentences extracting as a semantic relevance calculation problem,and proposes an Att-BiGRU/LSTM model.First,LSTM and GRU are used to encode the semantic expressed in a sentence.Then,the dissimilarity and similarity are captured with an Atten structure for semantic correlation. Last, adam optimizer is used to learn the model parameters.Experiment results show that Att-BiGRU model exceeds the baseline method of nearly 0.67 in terms of pearson,16.8% in terms of MSE on SemEval-SICK test dataset,which proves that the combination of the bidirectional and Atten structure can greatly improve the accuracy of the candidate sentences extraction,as well as the convergence rate.

Key words: Candidate sentences extracting, Gated recurrent unit, Long short term memory, Semantic correlation calculation