Words or Characters? Fine-grained Gating for Reading Comprehension
Table 4: Performance on the SQuAD dev set. Test set results are included in the brackets.
['Model', 'F1', 'Exact Match']
“Exact match” computes the ratio of questions that are answered correctly by strict string comparison, and the F1 score is computed on the token level. We can observe that both word-character fine-grained gating and document-query fine-grained gating can substantially improve the performance, leading to state-of-the-art results among published papers. Note that at the time of submission, the best score on the leaderboard is 0.716 in exact match and 0.804 in F1 without published papers. In this work, we focus on this general architecture to study the effectiveness of fine-grained gating mechanisms.