We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference and latent tag anchoring (required for weakly-supervised learning) are NP-hard problems. We propose two optimization algorithms based on constraint smoothing and conditional gradient to approximately solve these inference problems. Experimentally, our approach delivers state-of-the-art results on GeoQuery, Scan, and Clevr, both for i.i.d. splits and for splits that test for compositional generalization.

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.

Nous proposons un nouvel algorithme pour l’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante.

Les auto-encodeurs variationnels sont des modèles génératifs utiles pour apprendre des représentations latentes. En pratique, lorsqu’ils sont supervisés pour des tâches de génération de textes, ils ont tendance à ignorer les variables latentes lors du décodage. Nous proposons une nouvelle méthode de régularisation fondée sur le dropout « fraternel » pour encourager l’utilisation de ces variables latentes. Nous évaluons notre approche sur plusieurs jeux de données et observons des améliorations dans toutes les configurations testées.