We propose a novel methodology (namely, MuLER) that transforms any reference-based evaluation metric for text generation, such as machine translation (MT) into a fine-grained analysis tool. Given a system and a metric, MuLER quantifies how much the chosen metric penalizes specific error types (e.g., errors in translating names of locations). MuLER thus enables a detailed error analysis which can lead to targeted improvement efforts for specific phenomena. We perform experiments in both synthetic and naturalistic settings to support MuLER’s validity and showcase its usability in MT evaluation, and other tasks, such as summarization. Analyzing all submissions to WMT in 2014-2020, we find consistent trends. For example, nouns and verbs are among the most frequent POS tags. However, they are among the hardest to translate. Performance on most POS tags improves with overall system performance, but a few are not thus correlated (their identity changes from language to language). Preliminary experiments with summarization reveal similar trends.
@inproceedings{DBLP:conf/conll/KaridiCPA23,
author = {Taelin Karidi and
Leshem Choshen and
Gal Patel and
Omri Abend},
editor = {Jing Jiang and
David Reitter and
Shumin Deng},
title = {MuLER: Detailed and Scalable Reference-based Evaluation},
booktitle = {Proceedings of the 27th Conference on Computational Natural Language
Learning, CoNLL 2023, Singapore, December 6-7, 2023},
pages = {436--455},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://doi.org/10.18653/v1/2023.conll-1.29},
doi = {10.18653/V1/2023.CONLL-1.29},
timestamp = {Fri, 12 Apr 2024 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/conll/KaridiCPA23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.