Synthetic QA Corpora Generation with Roundtrip Consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins
Abstract
We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency. By pretraining on the resulting corpora we obtain significant improvements on SQuAD2 and NQ, establishing a new state-of-the-art on the latter. Our synthetic data generation models, for both question generation and answer extraction, can be fully reproduced by finetuning a publicly available BERT model on the extractive subsets of SQuAD2 and NQ. We also describe a more powerful variant that does full sequence-to-sequence pretraining for question generation, obtaining exact match and F1 at less than 0.1% and 0.4% from human performance on SQuAD2.- Anthology ID:
- P19-1620
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6168–6173
- Language:
- URL:
- https://aclanthology.org/P19-1620
- DOI:
- 10.18653/v1/P19-1620
- Cite (ACL):
- Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019. Synthetic QA Corpora Generation with Roundtrip Consistency. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6168–6173, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Synthetic QA Corpora Generation with Roundtrip Consistency (Alberti et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/landing_page/P19-1620.pdf
- Code
- additional community code
- Data
- Natural Questions