Words or Characters? Fine-grained Gating for Reading Comprehension
Table 1: Performance on the Twitter dataset. “word” and “char” means using word-level and character-level representations respectively.
['Model', 'Precision@1', 'Recall@10', 'Mean Rank']
We report three evaluation metrics including precision@1, recall@10, and mean rank. Our method outperforms character-level models used in Dhingra et al. 2.5 points in terms of precision, recall and mean rank respectively. Note that directly concatenating the token features with the character-level and word-level representations does not boost the performance, but using the token features to compute a gate (as done in fine-grained gating) leads to better results. This indicates that the benefit of fine-grained gating mainly comes from better modeling rather than using additional features.