Ricky Macharm


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).

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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