@inproceedings{nekoto-etal-2020-participatory,
title = "Participatory Research for Low-resourced Machine Translation: A Case Study in {A}frican Languages",
author = {Nekoto, Wilhelmina and
Marivate, Vukosi and
Matsila, Tshinondiwa and
Fasubaa, Timi and
Fagbohungbe, Taiwo and
Akinola, Solomon Oluwole and
Muhammad, Shamsuddeen and
Kabongo Kabenamualu, Salomon and
Osei, Salomey and
Sackey, Freshia and
Niyongabo, Rubungo Andre and
Macharm, Ricky and
Ogayo, Perez and
Ahia, Orevaoghene and
Berhe, Musie Meressa and
Adeyemi, Mofetoluwa and
Mokgesi-Selinga, Masabata and
Okegbemi, Lawrence and
Martinus, Laura and
Tajudeen, Kolawole and
Degila, Kevin and
Ogueji, Kelechi and
Siminyu, Kathleen and
Kreutzer, Julia and
Webster, Jason and
Ali, Jamiil Toure and
Abbott, Jade and
Orife, Iroro and
Ezeani, Ignatius and
Dangana, Idris Abdulkadir and
Kamper, Herman and
Elsahar, Hady and
Duru, Goodness and
Kioko, Ghollah and
Espoir, Murhabazi and
van Biljon, Elan and
Whitenack, Daniel and
Onyefuluchi, Christopher and
Emezue, Chris Chinenye and
Dossou, Bonaventure F. P. and
Sibanda, Blessing and
Bassey, Blessing and
Olabiyi, Ayodele and
Ramkilowan, Arshath and
{\"O}ktem, Alp and
Akinfaderin, Adewale and
Bashir, Abdallah},
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.195/",
doi = "10.18653/v1/2020.findings-emnlp.195",
pages = "2144--2160",
abstract = "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. {\textquoteleft}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 \url{https://github.com/masakhane-io/masakhane-mt}."
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
%A Nekoto, Wilhelmina
%A Marivate, Vukosi
%A Matsila, Tshinondiwa
%A Fasubaa, Timi
%A Fagbohungbe, Taiwo
%A Akinola, Solomon Oluwole
%A Muhammad, Shamsuddeen
%A Kabongo Kabenamualu, Salomon
%A Osei, Salomey
%A Sackey, Freshia
%A Niyongabo, Rubungo Andre
%A Macharm, Ricky
%A Ogayo, Perez
%A Ahia, Orevaoghene
%A Berhe, Musie Meressa
%A Adeyemi, Mofetoluwa
%A Mokgesi-Selinga, Masabata
%A Okegbemi, Lawrence
%A Martinus, Laura
%A Tajudeen, Kolawole
%A Degila, Kevin
%A Ogueji, Kelechi
%A Siminyu, Kathleen
%A Kreutzer, Julia
%A Webster, Jason
%A Ali, Jamiil Toure
%A Abbott, Jade
%A Orife, Iroro
%A Ezeani, Ignatius
%A Dangana, Idris Abdulkadir
%A Kamper, Herman
%A Elsahar, Hady
%A Duru, Goodness
%A Kioko, Ghollah
%A Espoir, Murhabazi
%A van Biljon, Elan
%A Whitenack, Daniel
%A Onyefuluchi, Christopher
%A Emezue, Chris Chinenye
%A Dossou, Bonaventure F. P.
%A Sibanda, Blessing
%A Bassey, Blessing
%A Olabiyi, Ayodele
%A Ramkilowan, Arshath
%A Öktem, Alp
%A Akinfaderin, Adewale
%A Bashir, Abdallah
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F nekoto-etal-2020-participatory
%X 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.
%R 10.18653/v1/2020.findings-emnlp.195
%U https://aclanthology.org/2020.findings-emnlp.195/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.195
%P 2144-2160
Markdown (Informal)
[Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://aclanthology.org/2020.findings-emnlp.195/) (Nekoto et al., Findings 2020)
ACL
- 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, and Abdallah Bashir. 2020. Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2144–2160, Online. Association for Computational Linguistics.