Data Augmentation for BERT Fine-Tuningin Open-Domain Question Answering
Table 1: Number of examples in each dataset. A example means a paragraph-question pair.
['[EMPTY]', 'SQuAD', 'TriviaQA', 'CMRC', 'DRCD']
Following Yang et al. As in previous work, exact match (EM) score and F1 score (at the token level) serve as the two primary evaluation metrics. In addition, we compute recall (R), the fraction of questions for which the correct answer appears in any retrieved paragraph; to make our results comparable to Yang et al. Note that this recall is not the same as the token-level recall component in the F1 score. Statistics for the datasets are shown in Table Thus, we eliminate the need to tune d by simply using all passages that do not contain the answer as negative examples.