David Ifeoluwa Adelani

Biography

David Adelani is an assistant professor at McGill University’s School of Computer Science under the Fighting Inequities initiative, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

Adelani’s research focuses on multilingual natural language processing with special attention to under-resourced languages.

Current Students

Henok Ademtew

Master's Research - McGill University

Master's Research - McGill University

Virginia Ceccatelli

Collaborating researcher - McGill University

David Guzmán Ramírez

PhD - McGill University

Research Intern - McGill University

Research Intern - McGill University

Yejin Jeon

Postdoctorate - McGill University

Senyu Li Li

PhD - McGill University

Yinhan Lu

Collaborating researcher - McGill University

Marie Maltais

PhD - McGill University

Damaris Stephanie NDJEBAYI

PhD - McGill University

Juhyun Oh

Collaborating Alumni - McGill University

Jessica Ojo

Master's Research - McGill University

Research Intern - McGill University

Vivek Verma

Professional Master's - Université de Montréal

Jessica Wang

Research Intern - McGill University

Tianyou Xie

Research Intern - McGill University

Tianyi Xu Xu

Research Intern - McGill University

Peter Yu

Collaborating Alumni - McGill University

Publications

Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models

Kenza Benkirane

Laura Gongas

Shahar Pelles

Naomi Fuchs

Joshua Darmon

Pontus Stenetorp

Eduardo Sánchez

Meta

2023-12-31

EMNLP (Findings) (published)

McGill NLP Group Submission to the MRL 2024 Shared Task: Ensembling Enhances Effectiveness of Multilingual Small LMs

Senyu Li

Hao Yu

Jessica Ojo

We present our systems for the three tasks and five languages included in the MRL 2024 Shared Task on Multilingual Multi-task Information Re… (see more)trieval: (1) Named Entity Recognition, (2) Free-form Question Answering, and (3) Multiple-choice Question Answering. For each task, we explored the impact of selecting different multilingual language models for fine-tuning across various target languages, and implemented an ensemble system that generates final outputs based on predictions from multiple fine-tuned models. All models are large language models fine-tuned on task-specific data. Our experimental results show that a more balanced dataset would yield better results. However, when training data for certain languages are scarce, fine-tuning on a large amount of English data supplemented by a small amount of “triggering data” in the target language can produce decent results.

2023-12-31

MRL (published)

Mitigating Translationese in Low-resource Languages: The Storyboard Approach

Garry Kuwanto

Eno-Abasi Urua

Priscilla A. Amuok

Shamsuddeen Hassan Muhammad

Aremu Anuoluwapo

Verrah Akinyi Otiende

Loice Emma Nanyanga

T. Nyoike

A. D. Akpan

Nsima Ab Udouboh

Idongesit Udeme Archibong

Idara Effiong Moses

Ifeoluwatayo A. Ige

Benjamin A. Ajibade

Olumide Benjamin Awokoya

Idris Abdulmumin

Saminu Mohammad Aliyu

Ruqayya Nasir Iro

Ibrahim Ahmad

Deontae Smith … (see 4 more)

Praise-EL Michaels

Derry Tanti Wijaya

Anietie U Andy

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which… (see more) can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

2023-12-31

International Conference on Language Resources and Evaluation (published)

SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

Hannah Liu

Xiaoyu Shen

Nikita Vassilyev

Jesujoba Oluwadara Alabi

Yanke Mao

Haonan Gao

Annie En-Shiun Lee

2023-12-31

EACL (1) (published)

Voices Unheard: NLP Resources and Models for Yor\`ub\'a Regional Dialects

Orevaoghene Ahia

Aremu Anuoluwapo

Diana Abagyan

Hila Gonen

Daud Abolade

Noah A. Smith

Yulia Tsvetkov

2023-12-31

EMNLP (published)

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages

Sebastian Ruder

Jonathan H. Clark

Alexander Gutkin

Mihir Kale

Min Ma

Massimo Nicosia

Shruti Rijhwani

Parker Riley

Jean Michel Amath Sarr

Xinyi Wang

John Frederick Wieting

Nitish Gupta

Anna Katanova

Christo Kirov

Dana L Dickinson

Brian Roark

Bidisha Samanta

Connie Tao

Vera Axelrod … (see 7 more)

Isaac Rayburn Caswell

Colin Cherry

Dan Garrette

Reeve Ingle

Melvin Johnson

Dmitry Panteleev

Partha Talukdar

Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) -- l… (see more)anguages for which NLP re-search is particularly far behind in meeting user needs -- it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks -- tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text),supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models

2023-11-30

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)

Better Quality Pre-training Data and T5 Models for African Languages

Akintunde Oladipo

Mofetoluwa Adeyemi

Orevaoghene Ahia

Abraham Toluwase Owodunni

Odunayo Ogundepo

Jimmy Lin

In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawl… (see more)s have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for

2023-10-06

EMNLP/2023/Conference (accepted)

Improving Language Plasticity via Pretraining with Active Forgetting

Yihong Chen

Kelly Marchisio

Roberta Raileanu

Pontus Stenetorp

Sebastian Riedel

Mikel Artetxe

Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performan… (see more)ce, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation, but also outperform standard ones in a low-data regime, particularly for languages that are distant from English. Code will be available at https://github.com/facebookresearch/language-model-plasticity.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

YORC: Yoruba Reading Comprehension dataset

Aremu Anuoluwapo

Jesujoba Oluwadara Alabi

In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehen… (see more)sion examination. We provide baseline results by performing cross-lingual transfer using existing English RACE dataset based on a pre-trained encoder-only model. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.

2023-08-17

ArXiv (preprint)

Consultative engagement of stakeholders toward a roadmap for African language technologies

Kathleen Siminyu

Jade Abbott

Kọ́lá Túbọ̀sún

Aremu Anuoluwapo

Blessing Kudzaishe Sibanda

Kofi Yeboah

Masabata Mokgesi-Selinga

Frederick R. Apina

Angela Thandizwe Mthembu

Arshath Ramkilowan

Babatunde Oladimeji

2023-08-10

Patterns (published)

NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification

Iyanuoluwa Shode

Jing Peng

Anna Feldman

Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there hav… (see more)e been progress in developing labelled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross-domain adaptation. We create a new dataset, Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian Pidgin, and Yoruba). We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. By leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While machine translation to low-resource languages are often of low quality, our analysis shows that sentiment related words are often preserved.

2023-05-17

ArXiv (preprint)

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

Shamsuddeen Hassan Muhammad

Idris Abdulmumin

Seid Muhie Yimam

Ibrahim Ahmad

Nedjma OUSIDHOUM

Abinew Ayele

Saif Mohammad

Meriem Beloucif

2023-04-12

ArXiv (preprint)