Sort by Structure: Language Model Ranking as Dependency Probing

Max Müller-Eberstein, Rob van der Goot, Barbara Plank


Abstract
Making an informed choice of pre-trained language model (LM) is critical for performance, yet environmentally costly, and as such widely underexplored. The field of Computer Vision has begun to tackle encoder ranking, with promising forays into Natural Language Processing, however they lack coverage of linguistic tasks such as structured prediction. We propose probing to rank LMs, specifically for parsing dependencies in a given language, by measuring the degree to which labeled trees are recoverable from an LM’s contextualized embeddings. Across 46 typologically and architecturally diverse LM-language pairs, our probing approach predicts the best LM choice 79% of the time using orders of magnitude less compute than training a full parser. Within this study, we identify and analyze one recently proposed decoupled LM—RemBERT—and find it strikingly contains less inherent dependency information, but often yields the best parser after full fine-tuning. Without this outlier our approach identifies the best LM in 89% of cases.
Anthology ID:
2022.naacl-main.93
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1296–1307
Language:
URL:
https://aclanthology.org/2022.naacl-main.93
DOI:
10.18653/v1/2022.naacl-main.93
Bibkey:
Cite (ACL):
Max Müller-Eberstein, Rob van der Goot, and Barbara Plank. 2022. Sort by Structure: Language Model Ranking as Dependency Probing. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1296–1307, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Sort by Structure: Language Model Ranking as Dependency Probing (Müller-Eberstein et al., NAACL 2022)
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PDF:
https://aclanthology.org/2022.naacl-main.93.pdf