Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation

Abstract

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. We report solid results on GeoQuery, Overnight, and WikiSQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks.

arXiv comment8 pages, 3 figures; accepted by AAAI Workshop 2019; accepted by International Conference of Semantic Computing (ICSC) 2019

References in corpus (18)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Improved Techniques for Training GANs
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Character-Aware Neural Language Models
• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• The Natural Language Decathlon: Multitask Learning as Question Answering
• Professor Forcing: A New Algorithm for Training Recurrent Networks
• SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
• Adversarial Learning for Neural Dialogue Generation
• Language to Logical Form with Neural Attention
• Compositional Semantic Parsing on Semi-Structured Tables
• A Transfer-Learnable Natural Language Interface for Databases
• Learning Structured Natural Language Representations for Semantic Parsing
• Cross-domain Semantic Parsing via Paraphrasing