Salomey Osei


2024

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AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang | David Adelani | Sweta Agrawal | Marek Masiak | Ricardo Rei | Eleftheria Briakou | Marine Carpuat | Xuanli He | Sofia Bourhim | Andiswa Bukula | Muhidin Mohamed | Temitayo Olatoye | Tosin Adewumi | Hamam Mokayed | Christine Mwase | Wangui Kimotho | Foutse Yuehgoh | Anuoluwapo Aremu | Jessica Ojo | Shamsuddeen Muhammad | Salomey Osei | Abdul-Hakeem Omotayo | Chiamaka Chukwuneke | Perez Ogayo | Oumaima Hourrane | Salma El Anigri | Lolwethu Ndolela | Thabiso Mangwana | Shafie Mohamed | Hassan Ayinde | Oluwabusayo Awoyomi | Lama Alkhaled | Sana Al-azzawi | Naome Etori | Millicent Ochieng | Clemencia Siro | Njoroge Kiragu | Eric Muchiri | Wangari Kimotho | Toadoum Sari Sakayo | Lyse Naomi Wamba | Daud Abolade | Simbiat Ajao | Iyanuoluwa Shode | Ricky Macharm | Ruqayya Iro | Saheed Abdullahi | Stephen Moore | Bernard Opoku | Zainab Akinjobi | Abeeb Afolabi | Nnaemeka Obiefuna | Onyekachi Ogbu | Sam Ochieng’ | Verrah Otiende | Chinedu Mbonu | Yao Lu | Pontus Stenetorp
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring 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).

2023

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Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo | Tajuddeen Gwadabe | Clara Rivera | Jonathan Clark | Sebastian Ruder | David Adelani | Bonaventure Dossou | Abdou Diop | Claytone Sikasote | Gilles Hacheme | Happy Buzaaba | Ignatius Ezeani | Rooweither Mabuya | Salomey Osei | Chris Emezue | Albert Kahira | Shamsuddeen Muhammad | Akintunde Oladipo | Abraham Owodunni | Atnafu Tonja | Iyanuoluwa Shode | Akari Asai | Anuoluwapo Aremu | Ayodele Awokoya | Bernard Opoku | Chiamaka Chukwuneke | Christine Mwase | Clemencia Siro | Stephen Arthur | Tunde Ajayi | Verrah Otiende | Andre Rubungo | Boyd Sinkala | Daniel Ajisafe | Emeka Onwuegbuzia | Falalu Lawan | Ibrahim Ahmad | Jesujoba Alabi | Chinedu Mbonu | Mofetoluwa Adeyemi | Mofya Phiri | Orevaoghene Ahia | Ruqayya Iro | Sonia Adhiambo
Findings of the Association for Computational Linguistics: EMNLP 2023

African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users. Cross-lingual open-retrieval question answering (XOR QA) systems – those that retrieve answer content from other languages while serving people in their native language—offer a means of filling this gap. To this end, we create Our Dataset, the first cross-lingual QA dataset with a focus on African languages. Our Dataset includes 12,000+ XOR QA examples across 10 African languages. While previous datasets have focused primarily on languages where cross-lingual QA augments coverage from the target language, Our Dataset focuses on languages where cross-lingual answer content is the only high-coverage source of answer content. Because of this, we argue that African languages are one of the most important and realistic use cases for XOR QA. Our experiments demonstrate the poor performance of automatic translation and multilingual retrieval methods. Overall, Our Dataset proves challenging for state-of-the-art QA models. We hope that the dataset enables the development of more equitable QA technology.

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AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR
Tobi Olatunji | Tejumade Afonja | Aditya Yadavalli | Chris Chinenye Emezue | Sahib Singh | Bonaventure F. P. Dossou | Joanne Osuchukwu | Salomey Osei | Atnafu Lambebo Tonja | Naome Etori | Clinton Mbataku
Transactions of the Association for Computational Linguistics, Volume 11

Africa has a very poor doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day—a heavy patient burden compared with developed countries—but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African English speech, 67,577 clips from 2,463 unique speakers across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.

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AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Muhammad | Idris Abdulmumin | Abinew Ayele | Nedjma Ousidhoum | David Adelani | Seid 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 | Tadesse Destaw Belay | Wendimu Messelle | Hailu Balcha | Sisay Chala | Hagos Gebremichael | Bernard Opoku | Stephen Arthur
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the AfriSenti datasets and discuss their usefulness.

2022

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AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
Bonaventure F. P. Dossou | Atnafu Lambebo Tonja | Oreen Yousuf | Salomey Osei | Abigail Oppong | Iyanuoluwa Shode | Oluwabusayo Olufunke Awoyomi | Chris Emezue
Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

In recent years, multilingual pre-trained language models have gained prominence due to their remarkable performance on numerous downstream Natural Language Processing tasks (NLP). However, pre-training these large multilingual language models requires a lot of training data, which is not available for African Languages. Active learning is a semi-supervised learning algorithm, in which a model consistently and dynamically learns to identify the most beneficial samples to train itself on, in order to achieve better optimization and performance on downstream tasks. Furthermore, active learning effectively and practically addresses real-world data scarcity. Despite all its benefits, active learning, in the context of NLP and especially multilingual language models pretraining, has received little consideration. In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework. Pretrained on a dataset significantly (14x) smaller than existing baselines, AfroLM outperforms many multilingual pretrained language models (AfriBERTa, XLMR-base, mBERT) on various NLP downstream tasks (NER, text classification, and sentiment analysis). Additional out-of-domain sentiment analysis experiments show that AfroLM is able to generalize well across various domains. We release the code source, and our datasets used in our framework at https://github.com/bonaventuredossou/MLM_AL.

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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann | Abhik Bhattacharjee | Abinaya Mahendiran | Alex Wang | Alexandros Papangelis | Aman Madaan | Angelina Mcmillan-major | Anna Shvets | Ashish Upadhyay | Bernd Bohnet | Bingsheng Yao | Bryan Wilie | Chandra Bhagavatula | Chaobin You | Craig Thomson | Cristina Garbacea | Dakuo Wang | Daniel Deutsch | Deyi Xiong | Di Jin | Dimitra Gkatzia | Dragomir Radev | Elizabeth Clark | Esin Durmus | Faisal Ladhak | Filip Ginter | Genta Indra Winata | Hendrik Strobelt | Hiroaki Hayashi | Jekaterina Novikova | Jenna Kanerva | Jenny Chim | Jiawei Zhou | Jordan Clive | Joshua Maynez | João Sedoc | Juraj Juraska | Kaustubh Dhole | Khyathi Raghavi Chandu | Laura Perez Beltrachini | Leonardo F . R. Ribeiro | Lewis Tunstall | Li Zhang | Mahim Pushkarna | Mathias Creutz | Michael White | Mihir Sanjay Kale | Moussa Kamal Eddine | Nico Daheim | Nishant Subramani | Ondrej Dusek | Paul Pu Liang | Pawan Sasanka Ammanamanchi | Qi Zhu | Ratish Puduppully | Reno Kriz | Rifat Shahriyar | Ronald Cardenas | Saad Mahamood | Salomey Osei | Samuel Cahyawijaya | Sanja Štajner | Sebastien Montella | Shailza Jolly | Simon Mille | Tahmid Hasan | Tianhao Shen | Tosin Adewumi | Vikas Raunak | Vipul Raheja | Vitaly Nikolaev | Vivian Tsai | Yacine Jernite | Ying Xu | Yisi Sang | Yixin Liu | Yufang Hou
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances. We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.

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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
Julia Kreutzer | Isaac Caswell | Lisa Wang | Ahsan Wahab | Daan van Esch | Nasanbayar Ulzii-Orshikh | Allahsera Tapo | Nishant Subramani | Artem Sokolov | Claytone Sikasote | Monang Setyawan | Supheakmungkol Sarin | Sokhar Samb | Benoît Sagot | Clara Rivera | Annette Rios | Isabel Papadimitriou | Salomey Osei | Pedro Ortiz Suarez | Iroro Orife | Kelechi Ogueji | Andre Niyongabo Rubungo | Toan Q. Nguyen | Mathias Müller | André Müller | Shamsuddeen Hassan Muhammad | Nanda Muhammad | Ayanda Mnyakeni | Jamshidbek Mirzakhalov | Tapiwanashe Matangira | Colin Leong | Nze Lawson | Sneha Kudugunta | Yacine Jernite | Mathias Jenny | Orhan Firat | Bonaventure F. P. Dossou | Sakhile Dlamini | Nisansa de Silva | Sakine Çabuk Ballı | Stella Biderman | Alessia Battisti | Ahmed Baruwa | Ankur Bapna | Pallavi Baljekar | Israel Abebe Azime | Ayodele Awokoya | Duygu Ataman | Orevaoghene Ahia | Oghenefego Ahia | Sweta Agrawal | Mofetoluwa Adeyemi
Transactions of the Association for Computational Linguistics, Volume 10

With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4). Lower-resource corpora have systematic issues: At least 15 corpora have no usable text, and a significant fraction contains less than 50% sentences of acceptable quality. In addition, many are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-proficient speakers, and supplement the human audit with automatic analyses. Finally, we recommend techniques to evaluate and improve multilingual corpora and discuss potential risks that come with low-quality data releases.

2021

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MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani | Jade Abbott | Graham Neubig | Daniel D’souza | Julia Kreutzer | Constantine Lignos | Chester Palen-Michel | Happy Buzaaba | Shruti Rijhwani | Sebastian Ruder | Stephen Mayhew | Israel Abebe Azime | Shamsuddeen H. Muhammad | Chris Chinenye Emezue | Joyce Nakatumba-Nabende | Perez Ogayo | Aremu Anuoluwapo | Catherine Gitau | Derguene Mbaye | Jesujoba Alabi | Seid Muhie Yimam | Tajuddeen Rabiu Gwadabe | Ignatius Ezeani | Rubungo Andre Niyongabo | Jonathan Mukiibi | Verrah Otiende | Iroro Orife | Davis David | Samba Ngom | Tosin Adewumi | Paul Rayson | Mofetoluwa Adeyemi | Gerald Muriuki | Emmanuel Anebi | Chiamaka Chukwuneke | Nkiruka Odu | Eric Peter Wairagala | Samuel Oyerinde | Clemencia Siro | Tobius Saul Bateesa | Temilola Oloyede | Yvonne Wambui | Victor Akinode | Deborah Nabagereka | Maurice Katusiime | Ayodele Awokoya | Mouhamadane MBOUP | Dibora Gebreyohannes | Henok Tilaye | Kelechi Nwaike | Degaga Wolde | Abdoulaye Faye | Blessing Sibanda | Orevaoghene Ahia | Bonaventure F. P. Dossou | Kelechi Ogueji | Thierno Ibrahima DIOP | Abdoulaye Diallo | Adewale Akinfaderin | Tendai Marengereke | Salomey Osei
Transactions of the Association for Computational Linguistics, Volume 9

We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

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Reusable Templates and Guides For Documenting Datasets and Models for Natural Language Processing and Generation: A Case Study of the HuggingFace and GEM Data and Model Cards
Angelina McMillan-Major | Salomey Osei | Juan Diego Rodriguez | Pawan Sasanka Ammanamanchi | Sebastian Gehrmann | Yacine Jernite
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Developing documentation guidelines and easy-to-use templates for datasets and models is a challenging task, especially given the variety of backgrounds, skills, and incentives of the people involved in the building of natural language processing (NLP) tools. Nevertheless, the adoption of standard documentation practices across the field of NLP promotes more accessible and detailed descriptions of NLP datasets and models, while supporting researchers and developers in reflecting on their work. To help with the standardization of documentation, we present two case studies of efforts that aim to develop reusable documentation templates – the HuggingFace data card, a general purpose card for datasets in NLP, and the GEM benchmark data and model cards with a focus on natural language generation. We describe our process for developing these templates, including the identification of relevant stakeholder groups, the definition of a set of guiding principles, the use of existing templates as our foundation, and iterative revisions based on feedback.

2020

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Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
Wilhelmina Nekoto | Vukosi Marivate | Tshinondiwa Matsila | Timi Fasubaa | Taiwo Fagbohungbe | Solomon Oluwole Akinola | Shamsuddeen Muhammad | Salomon Kabongo Kabenamualu | Salomey Osei | Freshia Sackey | Rubungo Andre Niyongabo | Ricky Macharm | Perez Ogayo | Orevaoghene Ahia | Musie Meressa Berhe | Mofetoluwa Adeyemi | Masabata Mokgesi-Selinga | Lawrence Okegbemi | Laura Martinus | Kolawole Tajudeen | Kevin Degila | Kelechi Ogueji | Kathleen Siminyu | Julia Kreutzer | Jason Webster | Jamiil Toure Ali | Jade Abbott | Iroro Orife | Ignatius Ezeani | Idris Abdulkadir Dangana | Herman Kamper | Hady Elsahar | Goodness Duru | Ghollah Kioko | Murhabazi Espoir | Elan van Biljon | Daniel Whitenack | Christopher Onyefuluchi | Chris Chinenye Emezue | Bonaventure F. P. Dossou | Blessing Sibanda | Blessing Bassey | Ayodele Olabiyi | Arshath Ramkilowan | Alp Öktem | Adewale Akinfaderin | Abdallah Bashir
Findings of the Association for Computational Linguistics: EMNLP 2020

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved. ‘Low-resourced’-ness is a complex problem going beyond data availability and reflects systemic problems in society. In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide. Despite immense improvements in MT over the past decade, MT is centered around a few high-resourced languages. As MT researchers cannot solve the problem of low-resourcedness alone, we propose participatory research as a means to involve all necessary agents required in the MT development process. We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution. Benchmarks, models, data, code, and evaluation results are released at https://github.com/masakhane-io/masakhane-mt.
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