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• W2963938442 abstract "We study the problem of semi-supervised question answering----utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text." @default.
• W2963938442 created "2019-07-30" @default.
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• W2963938442 date "2017-01-01" @default.
• W2963938442 modified "2023-10-17" @default.
• W2963938442 title "Semi-Supervised QA with Generative Domain-Adaptive Nets" @default.
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• W2963938442 doi "https://doi.org/10.18653/v1/p17-1096" @default.
• W2963938442 hasPublicationYear "2017" @default.
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