Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation
Sangwon Yoon, Yanghoon Kim, Kyomin Jung · 2021
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.