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

Vishwajeet Kumar, Manish Joshi, Ganesh Ramakrishnan, Yuan-Fang Li


Abstract
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.
Anthology ID:
2020.aacl-main.78
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
781–785
Language:
URL:
https://aclanthology.org/2020.aacl-main.78
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.78.pdf
Data
SQuAD