Papers › Learning Algebraic Recombination for Compositional Generalization

Learning Algebraic Recombination for Compositional Generalization

14 Jul 2021Findings (ACL) 2021 8arXiv:2107.06516archive 2025-07-28

Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang

Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamically recombining structured expressions in a recursive manner. However, most previous studies mainly concentrate on recombining lexical units, which is an important but not sufficient part of algebraic recombination. In this paper, we propose LeAR, an end-to-end neural model to learn algebraic recombination for compositional generalization. The key insight is to model the semantic parsing task as a homomorphism between a latent syntactic algebra and a semantic algebra, thus encouraging algebraic recombination. Specifically, we learn two modules jointly: a Composer for producing latent syntax, and an Interpreter for assigning semantic operations. Experiments on two realistic and comprehensive compositional generalization benchmarks demonstrate the effectiveness of our model. The source code is publicly available at https://github.com/microsoft/ContextualSP.

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microsoft/ContextualSP officialmentioned in papermentioned on GitHubpytorch report
thousfeet/lear mentioned on GitHubpytorch report

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Semantic Parsing

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TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Parsing CFQ LeAR Exact Match 90.9 #2 of 5 Archive leaderboard report

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