Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation

vishwajeet kumar1, Manish Joshi2, Ganesh Ramakrishnan3, Yuan-Fang Li4
1IBM Research Lab, 2Computer Science and Engineering, IIT Bombay, 3Department of Computer Science and Engineering, Indian Institute of Technology Bombay, 4Monash University


Question generation (QG) has recently attracted considerable attention. Most of the current neural models take as input only one or two sentences, and perform poorly when multiple sentences or complete paragraphs are given as input. However, in real-world scenarios it is very important to be able to generate high-quality questions from complete paragraphs. In this paper, we present a simple yet effective technique for answer-aware question generation from paragraphs. We augment a basic sequence-to-sequence QG model with dynamic, paragraph-specific dictionary and copy attention that is persistent across the corpus, without requiring features generated by sophisticated NLP pipelines or handcrafted rules. Our evaluation on SQuAD shows that our model significantly outperforms current state-of-the-art systems in question generation from paragraphs in both automatic and human evaluation. We achieve a 6-point improvement over the best system on BLEU-4, from 16.38 to 22.62.