Learning to Ask: Neural Question Generation for Reading Comprehension
Table 4: An estimate of categories of questions of the processed dataset and per-category performance comparison of the systems. The estimate is based on our analysis of the 346 pairs from the dev set. Categories are decided by the information needed to generate the question. Bold numbers represent the best performing method for a given metric. ∗Here, we leave out performance results for “w/ article” category (2 samples, 0.58%) and “not askable” category (33 samples, 9.54%).
['Category', '(%)', 'H&S BLEU-3', 'H&S BLEU-4', 'H&S METEOR', 'Ours BLEU-3', 'Ours BLEU-4', 'Ours METEOR', 'Ours + paragraph BLEU-3', 'Ours + paragraph BLEU-4', 'Ours + paragraph METEOR']
Our model which encodes paragraph information achieves the best performance on the questions of “w/ paragraph” category. This verifies the effectiveness of our paragraph-level model on the questions concerning information outside the sentence.