Portrait de David Ifeoluwa Adelani

David Ifeoluwa Adelani

Membre académique principal
david.adelani@mila.quebec
Chaire en IA Canada-CIFAR
McGill University
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Biographie

David Adelani est professeur adjoint à venir en science informatique et lutte contre les inégalités à l’Université McGill, et membre académique principal à Mila – Institut québécois d'intelligence artificielle. Ses recherches se concentrent sur le traitement multilingue du langage naturel, avec un accent particulier sur les langues sous-dotées en ressources.

Publications

Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning
Ernie Chang
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Vera Demberg
The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource l… (voir plus)anguages, human annotators are continuously tasked with the adaptation of resource-rich language utterances for each new domain. However, this prohibitive and impractical process can often be a bottleneck for low resource languages that are still without proper translation systems nor parallel corpus. In particular, it is difficult to obtain task-specific low resource language annotations for the English-derived creoles (e.g. Nigerian and Cameroonian Pidgin). To address this issue, we utilize the pretrained language models i.e. BART which has shown great potential in language generation/understanding – we propose to finetune the BART model to generate utterances in Pidgin by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives. We collected and released the first parallel Pidgin-English conversation corpus in two dialogue domains and showed that this simple and effective technique is suffice to yield impressive results for English-to-Pidgin generation, which are two closely-related languages.
2022-01-01
COLING (publié)
dblp.uni-trier.de
Findings of the WMT’22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages
David Ifeoluwa Adelani
Md Mahfuz Ibn Alam
Antonios Anastasopoulos
Akshita Bhagia
Marta R. Costa-jussa
Jesse Dodge
Fahim Faisal
Christian Federmann
Natalia N. Fedorova
Francisco S. Guzm'an
Sergey Koshelev
Jean Maillard
Vukosi Marivate
Jonathan Mbuya
Alexandre Mourachko
Safiyyah Saleem
Holger Schwenk
Guillaume Wenzek
We present the results of the WMT’22 SharedTask on Large-Scale Machine Translation Evaluation for African Languages. The shared taskinclud… (voir plus)ed both a data and a systems track, alongwith additional innovations, such as a focus onAfrican languages and extensive human evaluation of submitted systems. We received 14system submissions from 8 teams, as well as6 data track contributions. We report a largeprogress in the quality of translation for Africanlanguages since the last iteration of this sharedtask: there is an increase of about 7.5 BLEUpoints across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60.
2022-01-01
Conference on Machine Translation (publié)
dblp.uni-trier.de
Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
Marius Mosbach
Dietrich Klakow
and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to ap… (voir plus)plying LAFT on individual languages while requiring significantly less disk space. Finally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.
2022-01-01
arXiv.org (prépublication)
doi.org
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon
Lucile Saulnier
Thomas Wang
Christopher Akiki
Albert Villanova del Moral
Teven Le Scao
Leandro Von Werra
Chenghao Mou
Eduardo González Ponferrada
Huu Nguyen
Jörg Frohberg
Mario Šaško
Quentin Lhoest
Angelina McMillan-Major
Gérard Dupont
Stella Biderman
Anna Rogers
Loubna Ben allal
Francesco De Toni
Giada Pistilli … (voir 34 de plus)
Olivier Nguyen
Somaieh Nikpoor
Maraim Masoud
Pierre Colombo
Javier de la Rosa
Paulo Villegas
Tristan Thrush
Shayne Longpre
Sebastian Nagel
Leon Weber
Manuel Romero Muñoz
Jian Zhu
Daniel Van Strien
Zaid Alyafeai
Khalid Almubarak
Vu Minh Chien
Itziar Gonzalez-Dios
Aitor Soroa
Kyle Lo
Manan Dey
Pedro Ortiz Suarez
Aaron Gokaslan
Shamik Bose
David Ifeoluwa Adelani
Long Phan
Hieu Tran
Ian Yu
Suhas Pai
Jenny Chim
Violette Lepercq
Suzana Ilic
Margaret Mitchell
Sasha Luccioni
Yacine Jernite
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multili… (voir plus)ngual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.
openreview.net