Let’s Ask Again: Refine Network for Automatic Question Generation
Table 2: Comparsion of RefNet model with existing approaches and EAD model. Here * denotes our implementation of the corresponding work.
['[BOLD] Dataset', '[BOLD] Model', '[BOLD] n-gram', '[BOLD] n-gram', '[BOLD] n-gram', '[BOLD] n-gram', '[BOLD] n-gram', '[BOLD] n-gram', '[EMPTY]']
On BLEU-4 metric, RefNet beats the existing state-of-the-art model by 12.30%, 9.74%, 17.48%, and 3.71% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage) dataset. Also it outperforms EAD by 7.83%, 7.57%, 15.25% and 3.85% respectively on SQuAD (sentence), HOTPOT-QA, DROP and SQuAD (passage). In general, RefNet is consistently better than existing models across all n-gram scores (BLEU, ROUGE-L and METEOR). Along with n-gram scores, we also observe improvements on Q-BLEU4 as well, which as described earlier, gives a measure of both answerability and fluency.