An Open-Domain Question Answering Method Based on Hypothetical Semi-Supervised Learning

A semi-supervised learning and open-field technology, applied in the field of open-field question answering based on hypothetical semi-supervised learning, can solve problems such as too simple, no semantic analysis, loss, etc., and achieve the effect of avoiding information omission
CN108717413BActive Publication Date: 2021-10-08ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Publication Date
2021-10-08

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Abstract

The invention discloses an open domain question answering method based on hypothetical semi-supervised learning, including: (1) using information retrieval technology to extract articles related to the question from a corpus; (2) assuming that a given question answering training set comes with The article is the only positive label, and all articles extracted from the corpus are negative labels; (3) Construct a deep learning model, learn the characteristics of positive labels by training an article scorer, and train a reader to select the correct answer from the article ;(4) Perform correlation ranking of articles, send the first n articles with high correlation into the scorer for scoring and re-label according to the scores; (5) Repeat steps 3 and 4 until the model converges; (6) Model training After that, the open domain question answering application is performed. The invention can greatly improve the quality of article extraction and the accuracy of answers in the existing open domain question answering system without relying on additional manual annotation and external knowledge.
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Description

technical field

[0001] The invention relates to the field of natural language processing, in particular to an open domain question answering method based on hypothetical semi-supervised learning. Background technique

[0002] In recent years, open-domain question answering problems have become very popular and difficult problems in natural language processing. In this task, given a corpus and a question, the algorithmic system returns an answer from the corpus. The biggest difference between it and machine reading comprehension is that it adds the process of finding articles from the corpus in addition to answering questions based on the article. Open domain question answering systems are widely used, because traditional search engines can only meet the needs of a small number of people and most of the returned answers are just web page links rather than a specific answer. A question answering system that can extract articles from a large corpus and give ideal answers can ...

Claims

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