Self-Adapter at SemEval-2021 Task 10 - Entropy-based Pseudo-Labeler for Source-free Domain Adaptation.

SemEval@ACL/IJCNLP(2021)

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Abstract
Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.We study the domain adaption in the sequence labeling problem where the model trained on the source domain data is given.We propose two methods: Self-Adapter and Selective Classifier Training.Self-Adapter is a training method that uses sentence-level pseudolabels filtered by the self-entropy threshold to provide supervision to the whole model.Selective Classifier Training uses token-level pseudo-labels and supervises only the classification layer of the model.The proposed methods are evaluated on data provided by SemEval-2021 task 10 and Self-Adapter achieves 2 nd rank performance.
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Key words
Domain Adaptation,Semi-Supervised Learning,Transfer Learning,Meta-Learning,Unsupervised Learning
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