Improving Compositional Generalization in Semantic Parsing

• URL: http://arxiv.org/abs/2010.05647v1
• Date: Mon, 12 Oct 2020 12:34:58 GMT
• Title: Improving Compositional Generalization in Semantic Parsing
• Authors: Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, Jonathan Berant
• Abstract summary: Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. We investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization.
• Score: 54.4720965813889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes to new structures built of components observed during training, has sparked substantial interest. In this work, we investigate compositional generalization in semantic parsing, a natural test-bed for compositional generalization, as output programs are constructed from sub-components. We analyze a wide variety of models and propose multiple extensions to the attention module of the semantic parser, aiming to improve compositional generalization. We find that the following factors improve compositional generalization: (a) using contextual representations, such as ELMo and BERT, (b) informing the decoder what input tokens have previously been attended to, (c) training the decoder attention to agree with pre-computed token alignments, and (d) downsampling examples corresponding to frequent program templates. While we substantially reduce the gap between in-distribution and OOD generalization, performance on OOD compositions is still substantially lower.

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