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.

本文提出了 R＆R，一种学习数据扩充方案，它通过基于原始训练示例的原型生成模型的重组和生成示例的重新采样来实现大类组合泛化，显著提高了普适性，尤其是在对罕见或看不见的子序列需要组合泛化的环境中，如指令跟踪（SCAN）和形态分析（SIGMORPHON 2018）。