Author:
Shah Dhairya,Das Aniruddha,Shahane Aniket,Parikh Dharmik,Bari Pranit
Abstract
Incorporating SQL questions from normal language is a long-standing open issue and has been drawing in extensive intrigue as of late. Natural Language Interface (NLI) is the confluence of Natural Language Processing (NLP) and Human-Computer Interaction, which allows interaction between humans and computers through the utilization of Natural Language. Here we are gonna deal with the problem of automatic generation of Structured Query Language (SQL) queries. SQL is a database language for querying and manipulating relational databases. Despite the spectacular rise in the acceptance of relational databases, there is a fundamental limitaion to the ability to fetch data from those databases. One of the major reasons for this is the fact that the users of these relational databases need to comprehend convoluted structured query languages. In this body of work, we present an interface that allows users to interact with the databases using Natural Lanaguage as opposted to the conventional structure query languages.
Reference11 articles.
1. Zhong Victor, Xiong Caiming, Socher Richard, Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017)
2. Hwang Wonseok, Yim Jinyeong, Park Seunghyun, and Seo Minjoon: Exploration on WikiSQL with Table-Aware Word Contextualization. (2019)
3. Tao Yu, Zifan Li, Zhang Zilin, Zhang Rui, Radev Dragomir, TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation (2018)
4. Sun Yibo, Tang Duyu, Duan Nan, Jianshu Ji, Cao Guihong, Feng Xiaocheng, Qin Bing, Liu Ting, Ming Zhou Semantic Parsing with Syntax- and Table-Aware SQL Generation (2018)
5. Sun Yibo, Tang Duyu, Duan Nan, Jianshu Ji, Cao Guihong, Feng Xiaocheng, Qin Bing, Liu Ting, Ming Zhou Semantic Parsing with Syntax- and Table-Aware SQL Generation (2018)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献