Macro-Average: Rare Types Are Important Too

Thamme Gowda, Weiqiu You, Constantine Lignos, Jonathan May


Abstract
While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods’ outputs.
Anthology ID:
2021.naacl-main.90
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1138–1157
Language:
URL:
https://aclanthology.org/2021.naacl-main.90
DOI:
10.18653/v1/2021.naacl-main.90
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.90.pdf