Deep neural network and reinforcement learning-based generative machine reading comprehension method

Abstract

The invention discloses a deep neural network and reinforcement learning-based generative machine reading comprehension method. According to the method, texts and questions are encoded through an attention mechanism-combined deep neural network so as to form question information-fused text vector expressions, and decoding is carried out through a unidirectional LSTM decoder so as to gradually generate corresponding answer texts. According to the reading comprehension method, the advantages of extractive models and generative models are fused, training is carried out by adoption of a multi-taskcombined optimization manner, and a reinforcement learning method is used in the training process, so that benefit is brought to generate more correct and fluent answer texts.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a generative machine reading comprehension method based on deep neural network and reinforcement learning. Background technique [0002] As a subfield of natural language processing, machine reading comprehension has received great attention in recent years and has also made great progress. The emergence of richer data and more diverse and advanced models enables machines to better process natural language text input and, to a certain extent, be able to answer relevant questions raised on the input text. This is important for building more advanced natural language processing applications, such as automatic question answering (QA), dialogue system (DialogueSystem), providing more intelligent, efficient, and personalized search engine services, and even building real strong artificial intelligence. basic meaning. [0003] At present, most of the mainstream ma...

AI Technical Summary

Problems solved by technology

The S-NET model proposed in the paper "ChuanqiT, et al. S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension[J]. arXiv preprint arXiv: 1706.04815, 2017" adopts the method of first extraction and then synthesis, which can better can generate answers that conform to the form of natural language, but its extraction method has shortcomings, and it cannot use multiple effective information fragments in the text at the same time

The model proposed in the paper "Rajarshee Mitra.An Abstractiveapproach to Question Answering[J].arXiv preprint arXiv:1711.06238,2017" directly adopts the generation method, the model is more simplified, but it loses the extractive model mark to highlight the effective information in the original text The advantages

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