On the Importance of Diversity in Question Generation for QA
Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, Vittorio Castelli
Abstract
Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.- Anthology ID:
- 2020.acl-main.500
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5651–5656
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.500
- DOI:
- 10.18653/v1/2020.acl-main.500
- Cite (ACL):
- Md Arafat Sultan, Shubham Chandel, Ramón Fernandez Astudillo, and Vittorio Castelli. 2020. On the Importance of Diversity in Question Generation for QA. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5651–5656, Online. Association for Computational Linguistics.
- Cite (Informal):
- On the Importance of Diversity in Question Generation for QA (Sultan et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.500.pdf