Abstract
We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6% on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0%) and outperforming the out-of-domain baseline by 7.6%, without use of provided annotations.- Anthology ID:
- D17-1087
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 835–844
- Language:
- URL:
- https://aclanthology.org/D17-1087
- DOI:
- 10.18653/v1/D17-1087
- Cite (ACL):
- David Golub, Po-Sen Huang, Xiaodong He, and Li Deng. 2017. Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 835–844, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension (Golub et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-1087.pdf
- Code
- davidgolub/QuestionGeneration + additional community code
- Data
- MS MARCO, NewsQA, SQuAD