@inproceedings{lee-etal-2019-domain,
title = "Domain-agnostic Question-Answering with Adversarial Training",
author = "Lee, Seanie and
Kim, Donggyu and
Park, Jangwon",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5826/",
doi = "10.18653/v1/D19-5826",
pages = "196--202",
abstract = "Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model."
}
Markdown (Informal)
[Domain-agnostic Question-Answering with Adversarial Training](https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5826/) (Lee et al., 2019)
ACL