Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin
By Orevaoghene Ahia, Kelechi Ogueji. 2020
17 citations recorded by the source corpus.
Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Luke S. Zettlemoyer
- Graham Neubig
- Michael Auli
- Angela Fan
- Jason Aaron Edward Weston
- Edouard Grave
- Furu Wei
- Mohit Bansal
In Natural Language Processing
- Luke S. Zettlemoyer
- Graham Neubig
- Michael Auli
- Angela Fan
- Jason Aaron Edward Weston
- Edouard Grave
- Vishrav Chaudhary
- Furu Wei
Citations in the corpus
- Beyond English-Centric Multilingual Machine Translation (2020, methodology): “…exist, including JW300 (Agić and Vulić, 2019) used in the masakhane machine translation effort (∀ et al., 2020) and datasets for Nigerian Pidgin (Ahia and Ogueji, 2020), Wolof (Alla et al., 2020), Fon (Emezue and Dossou, 2020), Igbo (Ezeani et al., 2020), Amharic, Tigrigna, Afan-Oromo,…”
- Intriguing Properties of Compression on Multilingual Models (2022, background): “This is especially important since models for low-resourced languages are often deployed in resource-constrained environments§ (Ahia and Ogueji, 2020; Nekoto et al., 2020; Ahia et al., 2021; Ogueji et al., 2021; Moham-madshahi et al., 2022a).”