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arXiv · 1811.05303

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters

Denis Lukovnikov·Nilesh Chakraborty·Jens Lehmann·Asja Fischer
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Abstract

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this work, we extend a pointer-generator and investigate the order-matters problem in semantic parsing for SQL. Even though our model is a straightforward extension of a general-purpose pointer-generator, it outperforms early works for WikiSQL and remains competitive to concurrently introduced, more complex models. Moreover, we provide a deeper investigation of the potential order-matters problem that could arise due to having multiple correct decoding paths, and investigate the use of REINFORCE as well as a dynamic oracle in this context.

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Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer. 2018-11-13. Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters. https://arxiv.org/abs/1811.05303

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