AutoCAD: Automatically Generating Counterfactuals for Mitigating
Shortcut Learning
- URL: http://arxiv.org/abs/2211.16202v1
- Date: Tue, 29 Nov 2022 13:39:53 GMT
- Title: AutoCAD: Automatically Generating Counterfactuals for Mitigating
Shortcut Learning
- Authors: Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang, Jie Zhou and Minlie Huang
- Abstract summary: We present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
- Score: 70.70393006697383
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 https://github.com/thu-coai/AutoCAD.
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