The Effect of Natural Distribution Shift on Question Answering Models
This work addresses the need for robust evaluation metrics in NLP for researchers and practitioners, highlighting domain-specific vulnerabilities.
The study tackled the problem of question-answering models' generalization to new data by building four test sets from Wikipedia, New York Times, Reddit, and Amazon, finding no evidence of adaptive overfitting but average performance drops of 3.8, 14.0, and 17.4 F1 points across models on shifted domains, while humans showed little drop.
We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data. Our first test set is from the original Wikipedia domain and measures the extent to which existing systems overfit the original test set. Despite several years of heavy test set re-use, we find no evidence of adaptive overfitting. The remaining three test sets are constructed from New York Times articles, Reddit posts, and Amazon product reviews and measure robustness to natural distribution shifts. Across a broad range of models, we observe average performance drops of 3.8, 14.0, and 17.4 F1 points, respectively. In contrast, a strong human baseline matches or exceeds the performance of SQuAD models on the original domain and exhibits little to no drop in new domains. Taken together, our results confirm the surprising resilience of the holdout method and emphasize the need to move towards evaluation metrics that incorporate robustness to natural distribution shifts.