Sort by Structure: Language Model Ranking as Dependency Probing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AQ7PARVER" target="_blank" >RIV/00216208:11320/22:Q7PARVER - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.naacl-main.93" target="_blank" >https://aclanthology.org/2022.naacl-main.93</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2022.naacl-main.93" target="_blank" >10.18653/v1/2022.naacl-main.93</a>

Result language

angličtina

Original language name

Sort by Structure: Language Model Ranking as Dependency Probing

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

ISBN

978-1-955917-74-2

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

1296-1307

Publisher name

Association for Computational Linguistics

Place of publication

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Event location

Seattle, United States

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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