Papers by Clémentine Fourrier

5 papers
Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB-2.0 (2020.lrec-1)

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Challenge: Diachronic lexical information is increasingly used in historical linguistics and in NLP . etymological resources need to be fine-grained, large-coverage and accurate .
Approach: They propose guidelines to generate etymological lexical resources for each step of the life-cycle of an ethymology . they introduce EtymDB 2.0, an 'etiological database' generated from the Wiktionary .
Outcome: The proposed resources are generated for each step of the life-cycle of an etymological lexicon: creation, update, evaluation, dissemination, and exploitation.
Probing Multilingual Cognate Prediction Models (2022.findings-acl)

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Challenge: linguistic interpretations of cognate prediction have been based on external analysis (accuracy, raw results, errors).
Approach: They propose to use character-based machine translation models to store linguistic and diachronic information but not in previously assumed ways.
Outcome: The proposed model stores linguistic and diachronic information but does not achieve it in previously assumed ways.
La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America (2025.acl-long)

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Challenge: La Leaderboard is the first open-source leaderboard to evaluate generative Large Language Models (LLMs) in languages and language varieties of Spain and Latin America.
Approach: They propose to use La Leaderboard to evaluate generative Large Language Models in Spanish and Latin America.
Outcome: La Leaderboard is the first open-source leaderboard to evaluate generative LLMs in languages and language varieties of Spain and Latin America.
Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task? (2021.findings-acl)

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Challenge: Existing work on cognate prediction based on similarities of two languages has not studied their differences or optimized architectural choices.
Approach: They compare statistical and neural MT architectures to a bilingual setup to test their hypothesis . they use monolingual pretraining, backtranslation and multilinguality to test the hypothesis based on the results .
Outcome: The proposed architectures can be used to generate cognates in a given language . the proposed architecture can be employed with monolingual pretraining, backtranslation and multilinguality .
Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2025.acl-long)

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Challenge: Reliable multilingual evaluation is difficult and culturally appropriate evaluation is even harder to achieve.
Approach: They propose a multilingual evaluation framework that aims to mitigate these biases by improving translations and annotation practices.
Outcome: The proposed framework improves translation quality and cultural coverage and is culturally sensitive and culturally agnostic.

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