AI4D - African Language Dataset Challenge
By Kathleen Siminyu, Sackey Freshia, Jade Z. Abbott, V. Marivate. 2020
8 citations recorded by the source corpus.
As language and speech technologies become more advanced, the lack of fundamental digital resources for African languages, such as data, spell checkers and PoS taggers, means that the digital divide between these languages and others keeps growing. This work details the organisation of the AI4D - African Language Dataset Challenge, an effort to incentivize the creation, curation and uncovering to African language datasets through a competitive challenge, particularly datasets that are annotated or prepared for use in a downstream NLP task.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks
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
- Angela Fan
- Michael Auli
- Graham Neubig
- Edouard Grave
- Jason Aaron Edward Weston
- Furu Wei
- Michael Lewis
In Natural Language Processing
- Luke S. Zettlemoyer
- Angela Fan
- Michael Auli
- Graham Neubig
- Jason Aaron Edward Weston
- Edouard Grave
- Vishrav Chaudhary
- Armand Joulin
In Evaluation & Benchmarks
- Edouard Grave
- Naman Goyal
- Holger Schwenk
- Armand Joulin
- Guillaume Wenzek
- Vishrav Chaudhary
- Ahmed El-Kishky
- Sergey Edunov
Citations in the corpus
- Beyond English-Centric Multilingual Machine Translation (2020, background): “• Tatoeba — Tatoeba Challenge5 covers 692 test pairs from mixed domains where sentences are contributed and translated by volunteers online.”