Why only Micro-F1? Class Weighting of Measures for Relation Classification

David Harbecke, Yuxuan Chen, Leonhard Hennig, Christoph Alt


Abstract
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes. We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model.
Anthology ID:
2022.nlppower-1.4
Volume:
Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
nlppower
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–41
Language:
URL:
https://aclanthology.org/2022.nlppower-1.4
DOI:
10.18653/v1/2022.nlppower-1.4
Bibkey:
Cite (ACL):
David Harbecke, Yuxuan Chen, Leonhard Hennig, and Christoph Alt. 2022. Why only Micro-F1? Class Weighting of Measures for Relation Classification. In Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP, pages 32–41, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Why only Micro-F1? Class Weighting of Measures for Relation Classification (Harbecke et al., nlppower 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.nlppower-1.4.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.nlppower-1.4.mp4
Code
 dfki-nlp/weighting-schemes-report
Data
DocRED