The Effect of Natural Distribution Shift on Question Answering Models
arXiv:2004.14444
Abstract
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.
References in corpus (12)
- Machine Comprehension Using Match-LSTM and Answer Pointer
- Learning and Evaluating General Linguistic Intelligence
- Learning Recurrent Span Representations for Extractive Question Answering
- DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
- A Mutual Information Maximization Perspective of Language Representation Learning
- MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
- The Ladder: A Reliable Leaderboard for Machine Learning Competitions
- End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
- Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
- Phase Conductor on Multi-layered Attentions for Machine Comprehension
- Smarnet: Teaching Machines to Read and Comprehend Like Human
- The advantages of multiple classes for reducing overfitting from test set reuse
Cited by in corpus (5)
- Learning Transferable Visual Models From Natural Language Supervision
- Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
- Do Offline Metrics Predict Online Performance in Recommender Systems?
- QUACKIE: A NLP Classification Task With Ground Truth Explanations