Recent studies have shown the impressive efficacy of counterfactually
augmented data (CAD) for reducing NLU models' reliance on spurious features and
improving their generalizability. However, current methods still heavily rely
on human efforts or task-specific designs to generate counterfactuals, thereby
impeding CAD's applicability to a broad range of NLU tasks. In this paper, we
present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
AutoCAD first leverages a classifier to unsupervisedly identify rationales as
spans to be intervened, which disentangles spurious and causal features. Then,
AutoCAD performs controllable generation enhanced by unlikelihood training to
produce diverse counterfactuals. Extensive evaluations on multiple
out-of-domain and challenge benchmarks demonstrate that AutoCAD consistently
and significantly boosts the out-of-distribution performance of powerful
pre-trained models across different NLU tasks, which is comparable or even
better than previous state-of-the-art human-in-the-loop or task-specific CAD
methods. The code is publicly available at this https URL.

本文介绍了一个完全自动且任务不可知的 CAD 生成框架 AutoCAD，该框架结合分类器解析生成对抗数据，通过不太可能的训练生成多样性的对抗，从而系统提高了不同 NLU 任务的预训练模型在多个挑战基准测试中的翻译能力。