Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so. In this work, we study compositional generalization in classification tasks and present two main contributions. First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization. Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.

翻译：集成性一般化是指能够通过将已知组成部分合并,系统化地向新的数据分布推广。虽然人类似乎具有巨大的能力来将构成性、最先进的神经模型加以概括化。在这项工作中,我们在分类任务中研究集成性概括化,并提出两个主要贡献。首先,我们研究如何将自然语言序列和序列数据集转换为分类数据集,这也需要集成性概括化。第二,我们表明提供结构提示(具体地说,提供剖析树和实体链接,作为变形模型的注意面)有助于集成性。