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arXiv · 2310.14124

Structural generalization in COGS: Supertagging is (almost) all you need

Alban Petit·Caio Corro·François Yvon
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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 semantic parsing framework in several ways to alleviate this issue. Notably, we propose: (1) the introduction of a supertagging step with valency constraints, expressed as an integer linear program; (2) a 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, our results confirm that structural constraints are important for generalization in semantic parsing.

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Alban Petit, Caio Corro, François Yvon. 2023-10-21. Structural generalization in COGS: Supertagging is (almost) all you need. https://arxiv.org/abs/2310.14124

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