Learning to Ask: Neural Question Generation for Reading Comprehension
Table 2: Automatic evaluation results of different systems by BLEU 1–4, METEOR and ROUGEL. For a detailed explanation of the baseline systems, please refer to Section 5.3. The best performing system for each column is highlighted in boldface. Our system which encodes only sentence with pre-trained word embeddings achieves the best performance across all the metrics.
['Model', 'BLEU 1', 'BLEU 2', 'BLEU 3', 'BLEU 4', 'METEOR', 'ROUGEL']
To prove the effectiveness of our system, we compare it to several competitive systems. Next, we briefly introduce their approaches and the experimental setting to run them for our problem. Our model which only encodes sentence-level information achieves the best performance across all metrics. We note that IR performs poorly, indicating that memorizing the training set is not enough for the task. H&S system’s performance is on a par with DirectIn ’s, as it basically performs syntactic change without paraphrasing, and the overlap rate is also high.