DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AJW22F7F9" target="_blank" >RIV/00216208:11320/25:JW22F7F9 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204493211&partnerID=40&md5=55141a7c83509b4afc5d081bc6afa13c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204493211&partnerID=40&md5=55141a7c83509b4afc5d081bc6afa13c</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages

Popis výsledku v původním jazyce

Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, we propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties, which aggregates an extensive set of task-varied variety datasets (10 text-level tasks covering 281 varieties). This allows for a comprehensive evaluation of NLP system performance on different language varieties. We provide substantial evidence of performance disparities between standard and non-standard language varieties, and we also identify language clusters with larger performance divergence across tasks. We believe DIALECTBENCH provides a comprehensive view of the current state of NLP for language varieties and one step towards advancing it further. © 2024 Association for Computational Linguistics.

Název v anglickém jazyce

DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages

Popis výsledku anglicky

Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, we propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties, which aggregates an extensive set of task-varied variety datasets (10 text-level tasks covering 281 varieties). This allows for a comprehensive evaluation of NLP system performance on different language varieties. We provide substantial evidence of performance disparities between standard and non-standard language varieties, and we also identify language clusters with larger performance divergence across tasks. We believe DIALECTBENCH provides a comprehensive view of the current state of NLP for language varieties and one step towards advancing it further. © 2024 Association for Computational Linguistics.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. Annu. Meet. Assoc. Comput Linguist.

ISBN

979-889176094-3

ISSN

0736-587X

e-ISSN

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Počet stran výsledku

43

Strana od-do

14412-14454

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

Bangkok

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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