SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
Egoitz Laparra, Xin Su, Yiyun Zhao, Özlem Uzuner, Timothy A. Miller, Steven J. Bethard · 2021
This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021.The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints.Specifically, we consider the scenario where annotations exist for a domain but cannot be shared.Instead, participants are provided with models trained on that (source) data.Participants also receive some labeled data from a new (development) domain on which to explore domain adaptation algorithms.Participants are then tested on data representing a new (target) domain.We explored this scenario with two different semantic tasks: negation detection (a text classification task) and time expression recognition (a sequence tagging task).