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公开(公告)号：US20220383206A1
公开(公告)日：2022-12-01
申请号：US17826690
申请日：2022-05-27
Applicant: Google LLC
Inventor： Thang Minh Luong , Tu Thanh Vu , Quoc V. Le , Grady Hayes Simon
Abstract: Systems and methods can leverage task-specific unlabeled data to improve downstream performance in data-constrained scenarios. Given a target task, a first technique proposed herein, which can be referred to as task augmentation, uses unlabeled text from the target domain to synthesize a large amount of in-domain training data for an auxiliary task A second technique provides a self-training algorithm, where a model learns to improve itself using its predictions on unlabeled examples.
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