Salomon Kabongo Kabenamualu


2022

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LiSTra Automatic Speech Translation: English to Lingala Case Study
Salomon Kabongo Kabenamualu | Vukosi Marivate | Herman Kamper
Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference

In recent years there has been great interest in addressing the data scarcity of African languages and providing baseline models for different Natural Language Processing tasks (Orife et al., 2020). Several initiatives (Nekoto et al., 2020) on the continent uses the Bible as a data source to provide proof of concept for some NLP tasks. In this work, we present the Lingala Speech Translation (LiSTra) dataset, release a full pipeline for the construction of such dataset in other languages, and report baselines using both the traditional cascade approach (Automatic Speech Recognition - Machine Translation), and a revolutionary transformer based End-2-End architecture (Liu et al., 2020) with a custom interactive attention that allows information sharing between the recognition decoder and the translation decoder.

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.