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 athttps://github.com/masakhane-io/masakhane-mt.
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. InFindings of the Association for Computational Linguistics: EMNLP 2020, pages 2144–2160, Online. Association for Computational Linguistics.
@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}."}
%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
[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)
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. InFindings of the Association for Computational Linguistics: EMNLP 2020, pages 2144–2160, Online. Association for Computational Linguistics.