David Ifeoluwa Adelani

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

Jiayi Wang

Sweta Agrawal

Ricardo Rei

Eleftheria Briakou

Marine Carpuat

Marek Masiak

Xuanli He

Sofia Bourhim

Andiswa Bukula

Muhidin A. Mohamed

Temitayo Olatoye

Hamam Mokayede

Christine Mwase

Wangui Kimotho

Foutse Yuehgoh

Aremu Anuoluwapo

Jessica Ojo

Shamsuddeen Hassan Muhammad

Salomey Osei … (voir 37 de plus)

Abdul-Hakeem Omotayo

Chiamaka Ijeoma Chukwuneke

Perez Ogayo

Oumaima Hourrane

Salma El Anigri

Lolwethu Ndolela

Thabiso Mangwana

Shafie Abdi Mohamed

Ayinde Hassan

Oluwabusayo Olufunke Awoyomi

Lama Alkhaled

sana Sabah al-azzawi

Naome A. Etori

Millicent A. Ochieng

Clemencia Siro

Samuel Njoroge

Eric Muchiri

Wangari Kimotho

Lyse Naomi Wamba Momo

Daud Abolade

Simbiat Ajao

Tosin Adewumi

Iyanuoluwa Shode

Ricky Macharm

Ruqayya Nasir Iro

Saheed Salahudeen Abdullahi

Stephen E. Moore

Bernard Opoku

Zainab Akinjobi

Abeeb Afolabi

Nnaemeka Casmir Obiefuna

Onyekachi Ogbu

Sam Brian

Verrah Akinyi Otiende

CHINEDU EMMANUEL MBONU

Toadoum Sari Sakayo

Pontus Stenetorp

Despite the progress we have recorded in scaling multilingual machine translation (MT) models and evaluation data to several under-resourced… (voir plus) African languages, it is difficult to measure accurately the progress we have made on these languages because evaluation is often performed on n -gram matching metrics like BLEU that often have worse correlation with human judgments. Embedding-based metrics such as COMET correlate better; however, 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 a simplified MQM guideline for error-span annotation and direct assessment (DA) scoring for 13 typologi-cally diverse African languages. Furthermore, we develop A FRI COMET—a COMET evaluation metric for African languages by leveraging DA training data from high-resource languages and African-centric multilingual encoder (AfroXLM-Roberta) to create the state-of-the-art evaluation metric for African languages MT with respect to Spearman-rank correlation with human judgments ( +0 . 406 ).

2023-01-01

arXiv.org (prépublication)

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

Jiayi Wang

Sweta Agrawal

Ricardo Rei

Eleftheria Briakou

Marine Carpuat

Marek Masiak

Xuanli He

Sofia Bourhim

Andiswa Bukula

Muhidin A. Mohamed

Temitayo Olatoye

Hamam Mokayede

Christine Mwase

Wangui Kimotho

Foutse Yuehgoh

Anuoluwapo Aremu

Jessica Ojo

Shamsuddeen Hassan Muhammad

Salomey Osei … (voir 37 de plus)

Abdul-Hakeem Omotayo

Chiamaka Chukwuneke

Perez Ogayo

Oumaima Hourrane

Salma El Anigri

Lolwethu Ndolela

Thabiso Mangwana

Shafie Abdi Mohamed

Ayinde Hassan

Oluwabusayo Olufunke Awoyomi

Lama Alkhaled

sana Sabah al-azzawi

Naome A. Etori

Millicent A. Ochieng

Clemencia Siro

Samuel Njoroge

Eric Muchiri

Wangari Kimotho

Lyse Naomi Wamba Momo

Daud Abolade

Simbiat Ajao

Tosin P. Adewumi

Iyanuoluwa Shode

Ricky Macharm

Ruqayya Nasir Iro

Saheed Salahudeen Abdullahi

Stephen E. Moore

Bernard Opoku

Zainab Akinjobi

Abeeb Afolabi

Nnaemeka Casmir Obiefuna

Onyekachi Ogbu

Sam Brian

V. Otiende

CHINEDU EMMANUEL MBONU

Toadoum Sari Sakayo

Pontus Stenetorp

Despite the progress we have recorded in scaling multilingual machine translation (MT) models and evaluation data to several under-resourced… (voir plus) African languages, it is difficult to measure accurately the progress we have made on these languages because evaluation is often performed on n -gram matching metrics like BLEU that often have worse correlation with human judgments. Embedding-based metrics such as COMET correlate better; however, 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 a simplified MQM guideline for error-span annotation and direct assessment (DA) scoring for 13 typologi-cally diverse African languages. Furthermore, we develop A FRI COMET—a COMET evaluation metric for African languages by leveraging DA training data from high-resource languages and African-centric multilingual encoder (AfroXLM-Roberta) to create the state-of-the-art evaluation metric for African languages MT with respect to Spearman-rank correlation with human judgments ( +0 . 406 ).

2023-01-01

arXiv.org (preprint)

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Shamsuddeen Hassan Muhammad

Idris Abdulmumin

Abinew Ayele

Nedjma OUSIDHOUM

Seid Muhie Yimam

Ibrahim Ahmad

Meriem Beloucif

Saif Mohammad

Sebastian Ruder

Oumaima Hourrane

Alipio Jorge

Pavel Brazdil

Felermino Ali

Davis David

Salomey Osei

Bello Shehu-Bello

Falalu Lawan

Tajuddeen Gwadabe

Samuel Rutunda … (voir 7 de plus)

Tadesse Belay

Wendimu Baye Messelle

Hailu Balcha

Sisay Adugna Chala

Hagos Gebremichael

Bernard Opoku

Stephen Arthur

2023-01-01

EMNLP (publié)

Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023

Francesco Tinner

Chris Emezue

Mammad Hajili

Omer Goldman

Muhammad Farid Adilazuarda

Muhammad Dehan Al Kautsar

Aziza Mirsaidova

Müge Kural

Dylan Massey

Chiamaka Ijeoma Chukwuneke

CHINEDU EMMANUEL MBONU

Damilola Oluwaseun Oloyede

Kayode Olaleye

Jonathan Atala

Benjamin A. Ajibade

Saksham Bassi

Rahul Aralikatte

Najoung Kim

Duygu Ataman

Large language models (LLMs) excel in language understanding and generation, especially in English which has ample public benchmarks for var… (voir plus)ious natural language processing (NLP) tasks. Nevertheless, their reliability across different languages and domains remains uncertain. Our new shared task introduces a novel benchmark to assess the ability of multilingual LLMs to comprehend and produce language under sparse settings, particularly in scenarios with under-resourced languages, with an emphasis on the ability to capture logical, factual, or causal relationships within lengthy text contexts. The shared task consists of two sub-tasks crucial to information retrieval: Named Entity Recognition (NER) and Reading Comprehension (RC), in 7 data-scarce languages: Azerbaijani, Igbo, Indonesian, Swiss German, Turkish, Uzbek and Yorùbá, which previously lacked annotated resources in information retrieval tasks. Our evaluation of leading LLMs reveals that, despite their competitive performance, they still have notable weaknesses such as producing output in the non-target language or providing counterfactual information that cannot be inferred from the context. As more advanced models emerge, the benchmark will remain essential for supporting fairness and applicability in information retrieval systems.

2023-01-01

MRL (publié)

A FRI S ENTI : A B ENCHMARK T WITTER S ENTIMENT A NALYSIS D ATASET FOR A FRICAN L ANGUAGES

Shamsuddeen Hassan Muhammad

Idris Abdulmumin

Abinew Ayele

Nedjma OUSIDHOUM

Seid Muhie Yimam

Meriem Beloucif

Saif Mohammad

Sebastian Ruder

Oumaima Hourrane

Pavel Brazdil

Felermino D M A Ali

Davis David

Salomey Osei

Bello Shehu-Bello

Falalu Lawan

Tajuddeen Gwadabe

Samuel Rutunda

Tadesse Belay

Wendimu Baye Messelle … (voir 5 de plus)

Hailu Balcha

Sisay Adugna Chala

Hagos Gebremichael

Bernard Opoku

Stephen Arthur

Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from… (voir plus) four language families (Afro-Asiatic, English Creole, Indo European, and Niger-Congo). We describe the data collection methodology, annotation process, and related challenges when cu-rating each of the datasets. We also build different sentiment classification baseline models on the datasets and discuss their usefulness.

2023-01-01

(publié)

www.semanticscholar.org

MasakhaNEWS: News Topic Classification for African languages

Marek Masiak

Israel Abebe Azime

Jesujoba Oluwadara Alabi

Atnafu Lambebo Tonja

Christine Mwase

Odunayo Ogundepo

Bonaventure F. P. Dossou

Akintunde Oladipo

Doreen Nixdorf

Chris Emezue

sana Sabah al-azzawi

Blessing Kudzaishe Sibanda

Davis David

Lolwethu Ndolela

Jonathan Mukiibi

Tunde Oluwaseyi Ajayi

Tatiana Moteu Ngoli

Brian Odhiambo

Abraham Toluwase Owodunni … (voir 42 de plus)

Nnaemeka Casmir Obiefuna

Shamsuddeen Hassan Muhammad

Saheed Salahudeen Abdullahi

Mesay Gemeda Yigezu

Tajuddeen Gwadabe

Idris Abdulmumin

Mahlet Taye Bame

Oluwabusayo Olufunke Awoyomi

Iyanuoluwa Shode

Tolulope Anu Adelani

Habiba Abdulganiy Kailani

Abdul-Hakeem Omotayo

Adetola Adeeko

Afolabi Abeeb

Aremu Anuoluwapo

Olanrewaju Samuel

Clemencia Siro

Wangari Kimotho

Onyekachi Ogbu

CHINEDU EMMANUEL MBONU

Chiamaka Ijeoma Chukwuneke

Samuel Fanijo

Jessica Ojo

Oyinkansola Fiyinfoluwa Awosan

Tadesse Kebede Guge

Toadoum Sari Sakayo

Pamela Nyatsine

Freedmore Sidume

Oreen Yousuf

Mardiyyah Oduwole

USSEN ABRE KIMANUKA

Kanda Patrick Tshinu

Thina Diko

Siyanda Nxakama

Abdulmejid Tuni Johar

Sinodos Gebre

Muhidin A. Mohamed

Shafie Abdi Mohamed

Fuad Mire Hassan

Moges Ahmed Mehamed

Evrard Ngabire

Pontus Stenetorp

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individ… (voir plus)ual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.

2023-01-01

AfricaNLP (publié)

MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African languages

Cheikh M. Bamba Dione

Peter Nabende

Jesujoba Oluwadara Alabi

Thapelo Sindane

Happy Buzaaba

Shamsuddeen Hassan Muhammad

Chris Emezue

Perez Ogayo

Aremu Anuoluwapo

Catherine Gitau

Derguene Mbaye

Jonathan Mukiibi

Blessing Kudzaishe Sibanda

Bonaventure F. P. Dossou

Andiswa Bukula

Rooweither Mabuya

Allahsera Auguste Tapo

Edwin Munkoh-Buabeng

Victoire Memdjokam Koagne … (voir 24 de plus)

Fatoumata Ouoba Kabore

Amelia Taylor

Godson Kalipe

Tebogo Macucwa

Vukosi Marivate

Tajuddeen Gwadabe

Mboning Tchiaze Elvis

Ikechukwu Onyenwe

Gratien Atindogbe

Tolulope Anu Adelani

Idris Akinade

Olanrewaju Samuel

Marien Nahimana

Théogène Musabeyezu

Emile Niyomutabazi

Ester Chimhenga

Kudzai Gotosa

Patrick Mizha

Apelete Agbolo

Seydou Traore

Chinedu Uchechukwu

Aliyu Yusuf

Muhammad Abdullahi

Dietrich Klakow

In this paper, we present AfricaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the… (voir plus) challenges in annotating POS for these languages using the universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.

2023-01-01

ACL (1) (publié)

arxiv.org

MphayaNER: Named Entity Recognition for Tshivenda

Rendani Mbuvha

Tendani Mutavhatsindi

Tshimangadzo Rakhuhu

Aluwani Mauda

Tshifhiwa Joshua Maumela

Andisani Masindi

Seani Rananga

Vukosi Marivate

Tshilidzi Marwala

Named Entity Recognition (NER) plays a vital role in various Natural Language Processing tasks such as information retrieval, text classific… (voir plus)ation, and question answering. However, NER can be challenging, especially in low-resource languages with limited annotated datasets and tools. This paper adds to the effort of addressing these challenges by introducing MphayaNER, the first Tshivenda NER corpus in the news domain. We establish NER baselines by fine-tuning state-of-the-art models on MphayaNER. The study also explores zero-shot transfer between Tshivenda and other related Bantu languages, with Setswana, chiShona and Kiswahili showing the best results. Augmenting MphayaNER with Setwana data was also found to improve model performance significantly. Both MphayaNER and the baseline models are made publicly available.

2023-01-01

AfricaNLP (publié)

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Zheng-Xin Yong

Hailey Schoelkopf

Niklas Muennighoff

Alham Fikri Aji

Khalid Almubarak

M. Saiful Bari

Lintang A. Sutawika

Jungo Kasai

Ahmed Baruwa

Genta Indra Winata

Stella Biderman

Dragomir R. Radev

Vassilina Nikoulina

The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the be… (voir plus)nefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at https://github.com/bigscience-workshop/multilingual-modeling.

2022-12-19

ArXiv (prépublication)

arxiv.org

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Teven Le Scao

Angela Fan

Christopher Akiki

Ellie Pavlick

Suzana Ili'c

Daniel Hesslow

Roman Castagn'e

Alexandra Luccioni

François Yvon

Matthias Gall'e

J. Tow

Alexander M. Rush

Stella Biderman

Albert Webson

Pawan Sasanka Ammanamanchi

Thomas Wang

Benoı̂t Sagot

Niklas Muennighoff

Albert Villanova del Moral

Olatunji Ruwase … (voir 372 de plus)

Rachel Bawden

Stas Bekman

Angelina McMillan-Major

Iz Beltagy

Huu Nguyen

Lucile Saulnier

Samson Tan

Pedro Ortiz Suarez

Victor Sanh

Hugo Laurençon

Yacine Jernite

Julien Launay

Margaret Mitchell

Colin Raffel

Aaron Gokaslan

Adi Simhi

Aitor Soroa

Alham Fikri Aji

Amit Alfassy

Anna Rogers

Ariel Kreisberg Nitzav

Canwen Xu

Chenghao Mou

Chris Emezue

Christopher Klamm

Colin D. Leong

Daniel Van Strien

Dragomir R. Radev

Eduardo González Ponferrada

Efrat Levkovizh

Ethan Kim

Eyal Bar Natan

Francesco De Toni

Gérard Dupont

Germán Kruszewski

Giada Pistilli

Hady Elsahar

Hamza Benyamina

Hieu Tran

Ian W. Yu

Idris Abdulmumin

Isaac L. Johnson

Itziar Gonzalez-Dios

Javier de la Rosa

Jenny Chim

Jesse Dodge

Jian Zhu

Jonathan Chang

Jörg Frohberg

Josephine L. Tobing

J. Bhattacharjee

Khalid Almubarak

Kimbo Chen

Kyle Lo

Leandro Von Werra

Leon Weber

Long Phan

Loubna Ben allal

Ludovic Tanguy

Manan Dey

Manuel Romero Muñoz

Maraim Masoud

Mar'ia Grandury

Mario Šaško

Max Huang

Maximin Coavoux

Mayank Singh

Mike Tian-Jian Jiang

Vu Minh Chien

Mohammad Ali Jauhar

Mustafa Ghaleb

Nishant Subramani

Nora Kassner

Nurulaqilla Khamis

Olivier Nguyen

Omar Espejel

Ona de Gibert

Paulo Villegas

Peter Henderson

Pierre Colombo

Priscilla A. Amuok

Quentin Lhoest

Rheza Harliman

Rishi Bommasani

Roberto Luis L'opez

Rui Ribeiro

Salomey Osei

Sampo Pyysalo

Sebastian Nagel

Shamik Bose

Shamsuddeen Hassan Muhammad

Shanya Sharma Sharma

Shayne Longpre

Somaieh Nikpoor

S. Silberberg

Suhas Pai

Sydney Zink

Tiago Timponi Torrent

Timo Schick

Tristan Thrush

Valentin Danchev

Vassilina Nikoulina

Veronika Laippala

Violette Lepercq

Vrinda Prabhu

Zaid Alyafeai

Zeerak Talat

Arun Raja

Benjamin Heinzerling

Chenglei Si

Elizabeth E Salesky

Sabrina J. Mielke

Wilson Y. Lee

Abheesht Sharma

Andrea Santilli

Antoine Chaffin

Arnaud Stiegler

Debajyoti Datta

Eliza Szczechla

Gunjan Chhablani

Han Wang

Harshit Pandey

Hendrik. Strobelt

Jason Alan Fries

Jos Rozen

Leo Gao

Lintang A. Sutawika

M. Saiful Bari

Maged S. Al-shaibani

Matteo Manica

Nihal V. Nayak

Ryan Teehan

Samuel Albanie

Sheng Shen

Srulik Ben-David

Stephen H. Bach

Taewoon Kim

T. Bers

Thibault F'evry

Trishala Neeraj

Urmish Thakker

Vikas Raunak

Xiang Tang

Zheng-Xin Yong

Zhiqing Sun

Shaked Brody

Y. Uri

Hadar Tojarieh

Adam Roberts

Hyung Won Chung

Jaesung Tae

Jason Phang

Ofir Press

Conglong Li

D. Narayanan

Hatim Bourfoune

Jared Casper

Jeff Rasley

Max Ryabinin

Mayank Mishra

Minjia Zhang

Mohammad Shoeybi

Myriam Peyrounette

Nicolas Patry

Nouamane Tazi

Omar Sanseviero

Patrick von Platen

Pierre Cornette

Pierre Franccois Lavall'ee

R'emi Lacroix

Samyam Rajbhandari

Sanchit Gandhi

Shaden Smith

St'ephane Requena

Suraj Patil

Tim Dettmers

Ahmed Baruwa

Amanpreet Singh

Anastasia Cheveleva

Anne-Laure Ligozat

Arjun Subramonian

Aur'elie N'ev'eol

Charles Lovering

Dan Garrette

D. Tunuguntla

Ehud Reiter

Ekaterina Taktasheva

E. Voloshina

Eli Bogdanov

Genta Indra Winata

Hailey Schoelkopf

Jan-Christoph Kalo

Jekaterina Novikova

Jessica Zosa Forde

Xiangru Tang

Jungo Kasai

Ken Kawamura

Liam Hazan

Marine Carpuat

Miruna-adriana Clinciu

Najoung Kim

Newton Cheng

O. Serikov

Omer Antverg

Oskar van der Wal

Rui Zhang

Ruochen Zhang

Sebastian Gehrmann

Shachar Mirkin

S. Pais

Tatiana Shavrina

Thomas Scialom

Tian Yun

Tomasz Limisiewicz

Verena Teresa Rieser

Vitaly Protasov

V. Mikhailov

Yada Pruksachatkun

Yonatan Belinkov

Zachary Bamberger

Zdenvek Kasner

Zdeněk Kasner

A. Pestana

Amir Feizpour

Ammar Khan

Amy Faranak

A. Santos

Anthony Hevia

Antigona Unldreaj

Arash Aghagol

Arezoo Abdollahi

Aycha Tammour

Azadeh Hajihosseini

Bahareh Behroozi

Benjamin A. Ajibade

B. Saxena

Carlos Muñoz Ferrandis

Danish Contractor

D. Lansky

Davis David

Douwe Kiela

Duong Anh Nguyen

Edward Chwee Kheng. Tan

Emi Baylor

Ezinwanne Ozoani

F. Mirza

Frankline Ononiwu

Habib Rezanejad

H.A. Jones

Indrani Bhattacharya

Irene Solaiman

Irina Sedenko

Isar Nejadgholi

J. Passmore

Joshua Seltzer

Julio Bonis Sanz

Karen Fort

Livia Macedo Dutra

Mairon Samagaio

Maraim Elbadri

Margot Mieskes

Marissa Kumar Gerchick

Martha Akinlolu

Michael McKenna

Mike Qiu

M. Ghauri

Mykola Burynok

Nafis Abrar

Nazneen Fatema Rajani

Nour Elkott

N. Fahmy

Olanrewaju Samuel

Ran An

R. Kromann

Ryan Hao

Samira Hassan Alizadeh

Sarmad Shubber

Silas L. Wang

Sourav Roy

Sylvain Viguier

Thanh-Cong Le

Tobi Oyebade

T. Le

Yoyo Yang

Zach Nguyen

Abhinav R. Kashyap

Alfredo Palasciano

Alison Callahan

Anima Shukla

Antonio Miranda-Escalada

Ayush Kumar Singh

Benjamin Beilharz

Bo Wang

Caio Matheus Fonseca De Brito

Chenxi Zhou

Chirag Jain

Chuxin Xu

Cl'ementine Fourrier

Daniel Le'on Perin'an

Daniel Molano

Dian Yu

Enrique Manjavacas

Fabio Barth

Florian Fuhrimann

Gabriel Altay

Giyaseddin Bayrak

Gully Burns

Helena U. Vrabec

I. Bello

Isha Dash

J. Kang

John Michael Giorgi

Jonas Golde

J. Posada

Karthi Sivaraman

Lokesh Bulchandani

Lu Liu

Luisa Shinzato

Madeleine Hahn de Bykhovetz

Maiko Takeuchi

Marc Pamies

M. A. Castillo

Marianna Nezhurina

Mario Sanger

Matthias Samwald

Michael Joseph Cullan

Michael Weinberg

Michiel De Wolf

Mina Mihaljcic

Minna Liu

Moritz Freidank

Myungsun Kang

Natasha Seelam

Nathan Dahlberg

Nicholas Michio Broad

Nikolaus Muellner

Pascale Fung

Patricia Haller

Ramya Chandrasekhar

Patrick Haller

Renata Eisenberg

Robert Martin

Rodrigo Canalli

Rosaline Su

Ruisi Su

Samuel Cahyawijaya

Samuele Garda

Shlok S Deshmukh

Shubhanshu Mishra

Sid Kiblawi

Simon Ott

Sinee Sang-aroonsiri

Srishti Kumar

Stefan Schweter

Sushil Pratap Bharati

Tanmay Laud

Th'eo Gigant

Tomoya Kainuma

Wojciech Kusa

Yanis Labrak

Yashasvi Bajaj

Yash Venkatraman

Yifan Xu

Ying Xu

Yu Xu

Z. Tan

Zhongli Xie

Zifan Ye

Mathilde Le Bras

Younes Belkada

Thomas Wolf

2022-11-09

ArXiv (prépublication)

arxiv.org

Building Together - Towards 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

2022-01-01

Social Science Research Network (publié)