Simple and effective data augmentation for compositional generalization
Yuekun Yao Alexander Koller
Compositional generalization, the ability to predict complex meanings from
training on simpler sentences, poses challenges for powerful pretrained seq2seq
models. In this paper, we show that data augmentation methods that sample MRs
and backtranslate them can be effective for compositional generalization, but
only if we sample from the right distribution. Remarkably, sampling from a
uniform distribution performs almost as well as sampling from the test
distribution, and greatly outperforms earlier methods that sampled from the
training distribution. We further conduct experiments to investigate the reason
why this happens and where the benefit of such data augmentation methods come
from.