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.
@article{arxiv.1912.02145,
title = {An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering},
author = {Shayne Longpre and Yi Lu and Zhucheng Tu and Chris DuBois},
journal= {arXiv preprint arXiv:1912.02145},
year = {2019}
}
Comments
Accepted at the 2nd Workshop on Machine Reading for Question Answering