Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data -- particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. We describe R&R, a learned data augmentation scheme that enables a large category of compositional generalizations without appeal to latent symbolic structure. R&R has two components: recombination of original training examples via a prototype-based generative model and resampling of generated examples to encourage extrapolation. Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems -- instruction following (SCAN) and morphological analysis (SIGMORPHON 2018) -- where R&R enables learning of new constructions and tenses from as few as eight initial examples.

翻译：灵活神经序列模型在各种任务上超越了语法模型和基于自动的神经序列模型。 但是,神经模型在需要超出培训数据(特别是稀有或看不见的子序列)的构成性概括化环境中效果不佳。 过去的工作发现在这些环境中象征性的脚架(例如语法模型或自动成像)是必不可少的。 我们描述R&R, 这是一个学习的数据增强计划,它使大量类的构成性概括化能够不吸引潜在的象征结构。 R&R有两个组成部分:通过原型的基因模型重新组合原始培训范例,并重新采样生成范例以鼓励外推。培训一个普通神经序列模型,该模型通过重新组合和重新标本的示例来扩大数据组,极大地改进了两种语言处理问题的一般化 -- -- 教学后(SCAN)和形态分析(SIGMORPHON 2018) -- -- 在那里,R&R能够从很少的几个初步例子中学习新的构造和时态。