Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Abstract
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM). However, based on a pilot study, we observe three issues of existing general-purpose language models when they are applied to text-to-SQL semantic parsers: fail to detect column mentions in the utterances, fail to infer column mentions from cell values, and fail to compose complex SQL queries. To mitigate these issues, we present a model pre-training framework, Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. GAP MODEL is trained on 2M utterance-schema pairs and 30K utterance-schema-SQL triples, whose utterances are produced by generative models. Based on experimental results, neural semantic parsers that leverage GAP MODEL as a representation encoder obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-SQL benchmarks.
Cite
@article{arxiv.2012.10309,
title = {Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training},
author = {Peng Shi and Patrick Ng and Zhiguo Wang and Henghui Zhu and Alexander Hanbo Li and Jun Wang and Cicero Nogueira dos Santos and Bing Xiang},
journal= {arXiv preprint arXiv:2012.10309},
year = {2020}
}
Comments
Accepted to AAAI 2021