Words or Characters? Fine-grained Gating for Reading Comprehension
Table 2: Performance on the CBT dataset. The “GA word char concat” results are extracted from Dhingra et al. (2016a). Our results on fine-grained gating are based on a single model. “CN” and “NE” are two widely used question categories. “dev” means development set, and “test” means test set.
['Model', 'CN dev', 'CN test', 'NE dev', 'NE test']
We report the results on common noun (CN) questions and named entity (NE) questions, which are two widely used question categories in CBT. Our fine-grained gating approach achieves new state-of-the-art performance on both settings and outperforms the current state-of-the-art results by up to 1.76% without using ensembles. Our method outperforms the baseline GA reader by up to 2.4%, which indicates the effectiveness of the fine-grained gating mechanism. Consistent with the results on the Twitter dataset, using word-character fine-grained gating can substantially improve the performance over concatenation or scalar gating. Furthermore, we can see that document-query fine-grained gating also contributes significantly to the final results.