MELA: Multilingual Evaluation of Linguistic Acceptability
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AVDHW2J8W" target="_blank" >RIV/00216208:11320/25:VDHW2J8W - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204436622&partnerID=40&md5=936049d43bab424e20b243a2baa4b851" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204436622&partnerID=40&md5=936049d43bab424e20b243a2baa4b851</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
MELA: Multilingual Evaluation of Linguistic Acceptability
Original language description
In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability-MELA, with 46K samples covering 10 languages from a diverse set of language families. We establish LLM baselines on this benchmark, and investigate cross-lingual transfer in acceptability judgements with XLM-R. In pursuit of multilingual interpretability, we conduct probing experiments with fine-tuned XLM-R to explore the process of syntax capability acquisition. Our results show that GPT-4o exhibits a strong multilingual ability, outperforming fine-tuned XLM-R, while open-source multilingual models lag behind by a noticeable gap. Cross-lingual transfer experiments show that transfer in acceptability judgment is non-trivial: 500 Icelandic fine-tuning examples lead to 23 MCC performance in a completely unrelated language-Chinese. Results of our probing experiments indicate that training on MELA improves the performance of XLM-R on syntax-related tasks. https://github.com/sjtu-compling/MELA. © 2024 Association for Computational Linguistics.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc. Annu. Meet. Assoc. Comput Linguist.
ISBN
979-889176094-3
ISSN
0736-587X
e-ISSN
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Number of pages
17
Pages from-to
2658-2674
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
Bangkok
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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