To create models that are robust across a wide range of test inputs, training
datasets should include diverse examples that span numerous phenomena. Dynamic
adversarial data collection (DADC), where annotators craft examples that
challenge continually improving models, holds promise as an approach for
generating such diverse training sets. Prior work has shown that running DADC
over 1-3 rounds can help models fix some error types, but it does not
necessarily lead to better generalization beyond adversarial test data. We
argue that running DADC over many rounds maximizes its training-time benefits,
as the different rounds can together cover many of the task-relevant phenomena.
We present the first study of longer-term DADC, where we collect 20 rounds of
NLI examples for a small set of premise paragraphs, with both adversarial and
non-adversarial approaches. Models trained on DADC examples make 26% fewer
errors on our expert-curated test set compared to models trained on
non-adversarial data. Our analysis shows that DADC yields examples that are
more difficult, more lexically and syntactically diverse, and contain fewer
annotation artifacts compared to non-adversarial examples.

使用动态对抗数据采集（DADC）能够不断地生成具有挑战性的例子，获得更多的训练样本，可提高模型泛化能力，同时减少模型的错误率。我们进行了长期的 DADC 研究，并使用对抗性和非对抗性方法收集了 20 个 NLI 示例，该方法训练的模型在我们的测试集上减少了 26％的错误率。