An Encoder-Decoder Framework Translating Natural Language to Database Queries
This addresses the challenge of data retrieval for users by automating SQL query generation, though it appears incremental as it builds on existing neural translation methods.
The paper tackled the problem of translating natural language to SQL queries by integrating grammar rules into an encoder-decoder framework, resulting in significant improvements in accuracy and training efficiency over state-of-the-art solutions.
Machine translation is going through a radical revolution, driven by the explosive development of deep learning techniques using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this paper, we consider a special case in machine translation problems, targeting to convert natural language into Structured Query Language (SQL) for data retrieval over relational database. Although generic CNN and RNN learn the grammar structure of SQL when trained with sufficient samples, the accuracy and training efficiency of the model could be dramatically improved, when the translation model is deeply integrated with the grammar rules of SQL. We present a new encoder-decoder framework, with a suite of new approaches, including new semantic features fed into the encoder, grammar-aware states injected into the memory of decoder, as well as recursive state management for sub-queries. These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning. The empirical evaluation on real world database and queries show that our approach outperform state-of-the-art solution by a significant margin.