Papers by Daniel Whitenack
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages (2020.findings-emnlp)
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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
| Challenge: | 'Low-resourced'-ness is a complex problem that goes beyond data availability and reflects systemic problems in society. |
| Approach: | They propose to use machine translation to scale to low-resourced languages by using a dataset and a benchmarking system to measure their resource use. |
| Outcome: | The proposed approach allows participants without formal training to make a unique scientific contribution. |
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data (2022.acl-long)
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| Challenge: | Pre-trained language models are increasingly applied in ways that are agnostic to targeted downstream tasks. |
| Approach: | They propose a multi-modal approach to train language models using whatever text and/or audio data might be available in a language. |
| Outcome: | The proposed approach improves on pre-trained models on Swahili and Kinyarwanda data, with an improvement of up to 6% over models that are trained from scratch. |
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks (2022.emnlp-main)
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| Challenge: | In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families. |
| Approach: | They present a set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. |
| Outcome: | The Bloom Library datasets cover 363 languages across 32 language families. |