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 this https URL

本文提出 LeAR 模型作为神经模型的一种解决方案，为语义解析任务中的组成归纳学习代数重组提供了一种方法。