Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift
Neural Semi-supervised Learning under Domain Shift
Abstract: Deep neural networks excel at learning from labeled data. In contrast, learning from unlabeled data, especially under domain shift, which is common in many real-world applications, remains a challenge. In this talk, I will touch on three aspects of learning under domain shift: First I will discuss an approach to select relevant data for domain adaptation in order to minimize negative transfer. Secondly, I will show how classic bootstrapping algorithms can be applied to neural networks and that they make for strong baselines in this challenging setting. Finally, I will describe new methods to use language models for semi-supervised learning.
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