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        <title>Structural generalization in COGS: Supertagging is (almost) all you need</title>
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    <name type="personal">
        <namePart type="given">Alban</namePart>
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    <name type="personal">
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            <namePart type="given">Juan</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based parsing framework in several ways to alleviate this issue, notably: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) the reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, these results confirm that structural constraints are important for generalization in semantic parsing.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.69</identifier>
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%0 Conference Proceedings
%T Structural generalization in COGS: Supertagging is (almost) all you need
%A Petit, Alban
%A Corro, Caio
%A Yvon, François
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F petit-etal-2023-structural
%X In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples. In particular, several recent semantic parsing datasets have put forward important limitations of neural networks in cases where compositional generalization is required. In this work, we extend a neural graph-based parsing framework in several ways to alleviate this issue, notably: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) the reduction of the graph prediction problem to the maximum matching problem; (3) the design of an incremental early-stopping training strategy to prevent overfitting. Experimentally, our approach significantly improves results on examples that require structural generalization in the COGS dataset, a known challenging benchmark for compositional generalization. Overall, these results confirm that structural constraints are important for generalization in semantic parsing.
%R 10.18653/v1/2023.emnlp-main.69
%U https://aclanthology.org/2023.emnlp-main.69
%U https://doi.org/10.18653/v1/2023.emnlp-main.69
%P 1089-1101

Markdown (Informal)

[Structural generalization in COGS: Supertagging is (almost) all you need](https://aclanthology.org/2023.emnlp-main.69) (Petit et al., EMNLP 2023)

• Structural generalization in COGS: Supertagging is (almost) all you need (Petit et al., EMNLP 2023)

ACL

• Alban Petit, Caio Corro, and François Yvon. 2023. Structural generalization in COGS: Supertagging is (almost) all you need. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1089–1101, Singapore. Association for Computational Linguistics.