Question Generation Based on Grammar Knowledge and Fine-grained Classification

Yuan Sun, Sisi Liu, Zhengcuo Dan, Xiaobing Zhao


Abstract
Question generation is the task of automatically generating questions based on given context and answers, and there are problems that the types of questions and answers do not match. In minority languages such as Tibetan, since the grammar rules are complex and the training data is small, the related research on question generation is still in its infancy. To solve the above problems, this paper constructs a question type classifier and a question generator. We perform fine-grained division of question types and integrate grammatical knowledge into question type classifiers to improve the accuracy of question types. Then, the types predicted by the question type classifier are fed into the question generator. Our model improves the accuracy of interrogative words in generated questions, and the BLEU-4 on SQuAD reaches 17.52, the BLEU-4 on HotpotQA reaches 19.31, the BLEU-4 on TibetanQA reaches 25.58.
Anthology ID:
2022.coling-1.562
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6457–6467
Language:
URL:
https://aclanthology.org/2022.coling-1.562
DOI:
Bibkey:
Cite (ACL):
Yuan Sun, Sisi Liu, Zhengcuo Dan, and Xiaobing Zhao. 2022. Question Generation Based on Grammar Knowledge and Fine-grained Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6457–6467, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Question Generation Based on Grammar Knowledge and Fine-grained Classification (Sun et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.562.pdf
Data
HotpotQASQuAD