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.
@article{arxiv.1910.09342,
title = {Domain-agnostic Question-Answering with Adversarial Training},
author = {Seanie Lee and Donggyu Kim and Jangwon Park},
journal= {arXiv preprint arXiv:1910.09342},
year = {2019}
}