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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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).

Original languageEnglish (US)
Title of host publicationSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop
EditorsAlexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
PublisherAssociation for Computational Linguistics (ACL)
Pages348-356
Number of pages9
ISBN (Electronic)9781954085701
StatePublished - 2021
Event15th International Workshop on Semantic Evaluation, SemEval 2021 - Virtual, Bangkok, Thailand
Duration: Aug 5 2021Aug 6 2021

Publication series

NameSemEval 2021 - 15th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Conference

Conference15th International Workshop on Semantic Evaluation, SemEval 2021
Country/TerritoryThailand
CityVirtual, Bangkok
Period8/5/218/6/21

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing'. Together they form a unique fingerprint.

Cite this