Domain-agnostic Question-Answering with Adversarial Training [article]

Seanie Lee, Donggyu Kim, Jangwon Park
2019 arXiv   pre-print
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.
arXiv:1910.09342v2 fatcat:5khf7io3nfa5lcuyu2vk762iyu