Transferable Natural Language Interface to Structured Queries Aided by Adversarial Generation

A natural language interface (NLI) to structured query is intriguing due to its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning on source domain semantic parsing, and (b) data augmentation on target domain. We present a Structured Query Inference Network (SQIN) to enhance learning for domain adaptation, by separating schema information from NL and decoding SQL in a more structural-aware manner; we also propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue of lacking target domain data, by generating NL texts from recombined target-domain SQL queries. We report solid results on Geoquery, Overnight, and WIKISQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks. Our experiment is promising to significantly reduce human labor for transfer learning tasks.