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An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering
- Source :
- In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp. 220-227. 2019
- Publication Year :
- 2019
-
Abstract
- To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation. We find a simple negative sampling technique to be particularly effective, even though it is typically used for datasets that include unanswerable questions, such as SQuAD 2.0. When applied in conjunction with per-domain sampling, our XLNet (Yang et al., 2019)-based submission achieved the second best Exact Match and F1 in the MRQA leaderboard competition.<br />Comment: Accepted at the 2nd Workshop on Machine Reading for Question Answering
- Subjects :
- Computer Science - Computation and Language
Subjects
Details
- Database :
- arXiv
- Journal :
- In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp. 220-227. 2019
- Publication Type :
- Report
- Accession number :
- edsarx.1912.02145
- Document Type :
- Working Paper