Showcase

Papers

• Hannah Kirk, Bertie Vidgen, and Scott Hale. 2022.

  Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning.

  In Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022), pages 52–61, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Julius Gonsior, Christian Falkenberg, Silvio Magino, Anja Reusch, Maik Thiele, and Wolfgang Lehner. 2022.

  To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models.

  ArXiv, abs/2210.03005.
• Julia Romberg and Tobias Escher. 2022.

  Automated Topic Categorisation of Citizens’ Contributions: Reducing Manual Labelling Efforts Through Active Learning.

  In EGOV 2022: Electronic Government, pages 369–385, Cham. Springer International Publishing.
• Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022.

  Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers.

  In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203.

Tutorials

• 👂 Active learning for text classification with small-text