Open AccessResearch and Implementation of Open Domain Question Answering System Based on DuReader Dataset and BIDAF Model
Author(s) -
Zechen Guo,
Fucheng Wan,
Ning Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1769/1/012033
Subject(s) - computer science , question answering , field (mathematics) , open domain , set (abstract data type) , domain (mathematical analysis) , order (exchange) , information retrieval , artificial intelligence , world wide web , data science , mathematical analysis , mathematics , finance , pure mathematics , economics , programming language
At present, major search engines emerge in endlessly, and the pop-up result list of search keywords makes people dizzy. In order to improve this problem and make the question query more concise and convenient, this article is based on the DuReader data set and reproduces BiDAF (Bi-Directional Attention Flow) The model builds an intelligent question answering system in the open field. The final model can extract answers to questions more accurately after training. This article embeds deep learning technology into the system and uses intelligent chat to show them.