{"paper":{"title":"Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abdallah Bashir, Adewale Akinfaderin, Alp \\\"Oktem, Arshath Ramkilowan, Ayodele Olabiyi, Blessing Itoro Bassey, Blessing Sibanda, Bonaventure Dossou, Chris Emezue, Christopher Onyefuluchi, Daniel Whitenack, Elan Van Biljon, Espoir Murhabazi, Ghollah Kioko, Goodness Duru, Hady Elsahar, Herman Kamper, Idris Abdulkabir Dangana, Ignatius Ezeani, Iroro Orife, Jade Abbott, Jamiil Toure Ali, Jason Webster, Julia Kreutzer, Kathleen Siminyu, Kelechi Ogueji, Kevin Degila, Kolawole Tajudeen, Laura Jane Martinus, Lawrence Okegbemi, Masabata Mokgesi-Selinga, Mofe Adeyemi, Musie Meressa, Orevaoghene Ahia, Perez Ogayo, Ricky Macharm, Rubungo Andre Niyongabo, Sackey Freshia, Salomey Osei, Salomon Kabongo, Shamsuddeen Hassan Muhammad, Solomon Oluwole Akinola, Taiwo Fagbohungbe, Tajudeen Kolawole, Timi Fasubaa, Tshinondiwa Matsila, Vukosi Marivate, Wilhelmina Nekoto","submitted_at":"2020-10-05T21:50:38Z","abstract_excerpt":"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 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02353","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.02353/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}