Learning to Learn to be Right for the Right Reasons (2021)

Abstract: Improving model generalization on held-out data is one of the core objectives in common- sense reasoning. Recent work has shown that models trained on the dataset with superficial cues tend to perform well on the easy test set with superficial cues but perform poorly on the hard test set without superficial cues. Previous approaches have resorted to manual methods of encouraging models not to overfit to superficial cues. While some of the methods have improved performance on hard instances, they also lead to degraded performance on easy in- stances. Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and the hard test set without superficial cues. Using a meta-learning objective, we learn such a model that improves performance on both the easy test set and the hard test set. By evaluating our models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation, we show that our proposed method leads to improved performance on both the easy test set and the hard test set upon which we observe up to 16.5 percentage points improvement over the baseline.

## introduction pre-trained language models such as bert @xcite and roberta @xcite have enabled performance improvements on benchmarks of language understanding @xcite . however, improved performance is not only the result of increased ability to solve the benchmark tasks as intended, but also due to models' increased ability to "cheat" by relying on superficial cues @xcite @xcite . that is, even though models may perform better in terms of benchmark scores, they often are right for the wrong reasons @xcite and exhibit worse performance when prevented from exploiting superficial cues @xcite @xcite . the woman trembled. why? a) she was in a good mood. b) she was nervous. ## conclusion we propose to directly learn a model that performs well on both instances with superficial cues and instances without superficial cues via a meta-learning objective. we carefully evaluate our models, which are meta-learned to improve generalization, on two important commonsense benchmarks, finding that our proposed method considerably improves performance across all test sets.