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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.225</identifier>
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%0 Conference Proceedings
%T Improving Compositional Generalization in Semantic Parsing
%A Oren, Inbar
%A Herzig, Jonathan
%A Gupta, Nitish
%A Gardner, Matt
%A Berant, Jonathan
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F oren-etal-2020-improving
%X 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.
%R 10.18653/v1/2020.findings-emnlp.225
%U https://aclanthology.org/2020.findings-emnlp.225
%U https://doi.org/10.18653/v1/2020.findings-emnlp.225
%P 2482-2495

Markdown (Informal)

[Improving Compositional Generalization in Semantic Parsing](https://aclanthology.org/2020.findings-emnlp.225) (Oren et al., Findings 2020)

• Improving Compositional Generalization in Semantic Parsing (Oren et al., Findings 2020)

ACL

• Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, and Jonathan Berant. 2020. Improving Compositional Generalization in Semantic Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2482–2495, Online. Association for Computational Linguistics.