SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing

Egoitz Laparra, Xin Su, Yiyun Zhao, Özlem Uzuner, Timothy A. Miller, Steven Bethard

Abstract: 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).