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.

数据增强方法在预训练 seq2seq 模型中对于复合泛化很有效，但仅在从正确分布中进行采样时。从均匀分布中进行采样的表现几乎与从测试分布中采样相当，并且明显优于从训练分布中采样的先前方法。我们进一步进行实验证明了这种情况发生的原因和这种数据增强方法带来的好处。

简洁有效的数据增强方法用于组合泛化

Simple and effective data augmentation for compositional generalization

Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.

本文提出一种新的框架，使用自动质量控制措施并评估不同 MR 来引出数据，从而通过众包获得高质量的 NLG 训练数据。研究表明，与逻辑 MR 相比，使用图像 MR 可以收集到更好的数据，这些数据被评为更自然、更信息丰富、更好地措辞，平均质量评分显著提高。