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Presentation at Bucharest Deep Learning meetup, 2nd April 2018
The quest for a simpler, more intuitive interface to query databases and other sources of structured information is one of the main practical applications of natural language processing. Developing a natural language interface for databases (NLIDB), able to process text queries directly, would be the optimal solution with regards to ease of use, requiring no additional knowledge and allowing non-technical persons to interact with the data directly.
During this talk we will explore the recent advances (datasets and methods / architectures) that allow developing deep neural network solutions for the NLIDB task. We will present two large datasets for this task, WikiSQL  and SENLIDB , which have been used to build several neural architectures for neural NLIDBs. The talk will present not only the differences between the proposed solutions, but also the important differences between the two datasets.
Some of the architectures proposed for SQL generation from natural language can also be used for code generation, question answering over linked data and other similar tasks.
 Zhong, V., Xiong, C., & Socher, R. (2017). Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. arXiv preprint arXiv:1709.00103. - https://arxiv.org/pdf/1709.00103.pdf
 Xu, X., Liu, C., & Song, D. (2017). SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning. arXiv preprint arXiv:1711.04436. - https://arxiv.org/pdf/1711.04436.pdf
 Brad, F., Iacob, R., Hosu, I., & Rebedea, T. (2017). Dataset for a Neural Natural Language Interface for Databases (NNLIDB). arXiv preprint arXiv:1707.03172. - https://arxiv.org/pdf/1707.03172.pdf
 Newer results using dual encoders for the SENLIDB dataset (paper under review)