Abstract
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.
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