Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

Autor:	Nekoto, Wilhelmina, Marivate, Vukosi, Matsila, Tshinondiwa, Fasubaa, Timi, Kolawole, Tajudeen, Fagbohungbe, Taiwo, Akinola, Solomon Oluwole, Muhammad, Shamsuddeen Hassan, Kabongo, Salomon, Osei, Salomey, Freshia, Sackey, Niyongabo, Rubungo Andre, Macharm, Ricky, Ogayo, Perez, Ahia, Orevaoghene, Meressa, Musie, Adeyemi, Mofe, Mokgesi-Selinga, Masabata, Okegbemi, Lawrence, Martinus, Laura Jane, Tajudeen, Kolawole, Degila, Kevin, Ogueji, Kelechi, Siminyu, Kathleen, Kreutzer, Julia, Webster, Jason, Ali, Jamiil Toure, Abbott, Jade, Orife, Iroro, Ezeani, Ignatius, Dangana, Idris Abdulkabir, Kamper, Herman, Elsahar, Hady, Duru, Goodness, Kioko, Ghollah, Murhabazi, Espoir, van Biljon, Elan, Whitenack, Daniel, Onyefuluchi, Christopher, Emezue, Chris, Dossou, Bonaventure, Sibanda, Blessing, Bassey, Blessing Itoro, Olabiyi, Ayodele, Ramkilowan, Arshath, Öktem, Alp, Akinfaderin, Adewale, Bashir, Abdallah
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 under https://github.com/masakhane-io/masakhane-mt. Comment: Findings of EMNLP 2020; updated benchmarks
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.02353 Zobrazit plný text záznamu View this record from Arxiv