Unifying Question Answering, Text Classification, and Regression via Span Extraction
Table 3: Exact match scores on the development set for a set of question answering tasks. Bold marks the best performance for a task. Note that SpEx-BERT and BERT are equivalent for the question answering task.
['[EMPTY]', 'SQuAD', 'ZRE', 'SRL', 'CQA']
S5SS0SSS0Px3 STILTs improves question answering as well. The transferability of question answering datasets is well-known. We further discover that through the formulation of SpEx-BERT, classification datasets also help question answering datasets. In particular, MNLI improves the scores of almost all datasets over their baselines. For SQuAD, the benefit of STILTs with the classification dataset MNLI is almost as much as the question answering dataset TriviaQA.