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• W2984402309 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." @default.
• W2984402309 created "2019-11-22" @default.
• W2984402309 creator A5066744863 @default.
• W2984402309 creator A5070831085 @default.
• W2984402309 creator A5080512264 @default.
• W2984402309 date "2019-01-01" @default.
• W2984402309 modified "2023-09-26" @default.
• W2984402309 title "Domain-agnostic Question-Answering with Adversarial Training" @default.
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• W2984402309 doi "https://doi.org/10.18653/v1/d19-5826" @default.
• W2984402309 hasPublicationYear "2019" @default.
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