Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Artificial Intelligence
2018-11-14 v1 Computation and Language
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.
Keywords
Cite
@article{arxiv.1811.05303,
title = {Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters},
author = {Denis Lukovnikov and Nilesh Chakraborty and Jens Lehmann and Asja Fischer},
journal= {arXiv preprint arXiv:1811.05303},
year = {2018}
}