Learning to Synthesize Data for Semantic Parsing

• URL: http://arxiv.org/abs/2104.05827v1
• Date: Mon, 12 Apr 2021 21:24:02 GMT
• Title: Learning to Synthesize Data for Semantic Parsing
• Authors: Bailin Wang, Wenpeng Yin, Xi Victoria Lin and Caiming Xiong
• Abstract summary: We propose a generative model which models the composition of programs and maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. We evaluate our method in both in-domain and out-of-domain settings of text-to-Query parsing on the standard benchmarks of GeoQuery and Spider.
• Score: 57.190817162674875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to a better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.

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