Publications

AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages

Jiayi Wang

David Ifeoluwa Adelani

Sweta Agrawal

Marek Masiak

Ricardo Rei

Eleftheria Briakou

Marine Carpuat

Xuanli He

Sofia Bourhim

Andiswa Bukula

Muhidin A. Mohamed

Temitayo Olatoye

Tosin Adewumi

Hamam Mokayed

Christine Mwase

Wangui Kimotho

Foutse Yuehgoh

Aremu Anuoluwapo

Jessica Ojo

Shamsuddeen Hassan Muhammad … (voir 41 de plus)

Salomey Osei

Abdul-Hakeem Omotayo

Chiamaka Ijeoma Chukwuneke

Perez Ogayo

Oumaima Hourrane

Salma El Anigri

Lolwethu Ndolela

Thabiso Mangwana

Shafie Abdi Mohamed

Hassan Ayinde

Ayinde Hassan

Oluwabusayo Olufunke Awoyomi

Lama Alkhaled

sana Sabah al-azzawi

Naome Etori

Millicent Ochieng

Clemencia Siro

Samuel Njoroge

Njoroge Kiragu

Eric Muchiri

Wangari Kimotho

Lyse Naomi Wamba

Daud Abolade

Simbiat Ajao

Iyanuoluwa Shode

Ricky Macharm

Ruqayya Nasir Iro

Saheed Salahudeen Abdullahi

Stephen Moore

Bernard Opoku

Zainab Akinjobi

Abeeb Afolabi

Nnaemeka Casmir Obiefuna

Onyekachi Ogbu

Sam Brian

Sam Ochieng’

Verrah Akinyi Otiende

CHINEDU EMMANUEL MBONU

Toadoum Sari Sakayo

Yao Lu

Pontus Stenetorp

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measur… (voir plus)ing this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).

2024-06-01

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (publié)

doi.org

arxiv.org

Better entity matching with transformers through ensembles

Jwen Fai Low

Benjamin Fung

Pulei Xiong

2024-06-01

Knowledge-Based Systems (publié)

doi.org

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Philipp Thölke

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

Karim Jerbi

2024-06-01

bioRxiv (prépublication)

doi.org

Evaluating In-Context Learning of Libraries for Code Generation

Arkil Patel

Siva Reddy

Dzmitry Bahdanau

Pradeep Dasigi

2024-06-01

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (publié)

doi.org

arxiv.org

Immunotherapeutic targeting of surfaceome heterogeneity in AML.

Marie-Eve Bordeleau

Éric Audemard

Arnaud Metois

Louis Theret

Véronique Lisi

Azer Farah

Jean-Francois Spinella

Jalila Chagraoui

Ossama Moujaber

Léo Aubert

Banafsheh Khakipoor

Laure Mallinger

Isabel Boivin

Nadine Mayotte

Azadeh Hajmirza

Eric Bonneil

Francois Béliveau

Sybille Pfammatter

Albert Feghaly

Geneviève Boucher … (voir 9 de plus)

Patrick Gendron

Pierre Thibault

Frederic Barabe

Sébastien Lemieux

Guillaume Richard-Carpentier

Josée Hébert

Vincent-Philippe Lavallee

Philippe Roux

Guy Sauvageau

2024-06-01

Cell Reports (publié)

doi.org

Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study

Abbie Naus

Madeleine Carroll

Ayla Gerk

David P. Mooney

Natalie L. Yanchar

Julia Ferreira

Dan Poenaru

Karen E. Gripp

Caroline Ouellet

Fabio Botelho

2024-06-01

Journal of Surgical Research (publié)

doi.org

Methods, Applications, and Directions of Learning-to-Rank in NLP Research

Justin Lee

Gabriel Bernier-Colborne

Tegan Maharaj

Sowmya Vajjala

Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web s… (voir plus)earch and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.

2024-06-01

Findings of the Association for Computational Linguistics: NAACL 2024 (publié)

doi.org

"One-Size-Fits-All"? Examining Expectations around What Constitute"Fair"or"Good"NLG System Behaviors

Li Lucy

Su Lin Blodgett

Milad Shokouhi

Hanna Wallach

Alexandra Olteanu

Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to beh… (voir plus)ave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case studies, in which we perturb different types of identity-related language features (names, roles, locations, dialect, and style) in NLG system inputs. Through these cases studies, we examine people's expectations of system behaviors, and surface potential caveats of these contrasting yet commonly held assumptions. We find that motivations for adaptation include social norms, cultural differences, feature-specific information, and accommodation; in contrast, motivations for invariance include perspectives that favor prescriptivism, view adaptation as unnecessary or too difficult for NLG systems to do appropriately, and are wary of false assumptions. Our findings highlight open challenges around what constitute"fair"or"good"NLG system behaviors.

2024-06-01

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (publié)

doi.org

arxiv.org

Pioneering women in nuclear and radiation sciences

Mirta Dumancic

Shirin A. Enger

2024-06-01

Radiotherapy and Oncology (publié)

doi.org

A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform.

Pascal Thériault-Lauzier

Denis Cobin

Olivier Tastet