Understand
Recent advances in neural machine translation (NMT) have led to state-of-the-art results for many European-based translation tasks.
- However, despite these advances, there is has been little focus in applying these methods to African languages.
- In this paper, we seek to address this gap by creating an NMT benchmark BLEU score between English and the ten remaining official languages in South Africa.
Built on
“Afrikaans: considering origins”
Paul Roberge · 2002
Earlier work this paper cites.
“Neural Machine Translation of Rare Words with Subword Units”
Rico Sennrich, Barry Haddow and Alexandra Birch · 2015
Earlier work this paper cites.
“Community Survey 2016 in Brief”
StatsSA · 2016
Earlier work this paper cites.
“Attention is All you Need”
Ashish Vaswani et al · 2017
Earlier work this paper cites.
Similar
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
Cited alongside, same era.
“Benchmarking Neural Machine Translation for Southern African Languages”
Jade Abbott and Laura Martinus · 2019
Cited alongside, same era.
“JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages”
Zeljko Agi“’c and Ivan Vuli“’c · 2019
Cited alongside, same era.
“Ethnologue: Languages of the World”
David Eberhard, Gary Simons and Charles Fenning
Cited in the paper.
Then
“English in Multilingual South Africa: The Linguistics of Contact and Change”
Raymond Hickey · 2019
Later among the works it cites.
“Joey NMT: A Minimalist NMT Toolkit for Novices”
Julia Kreutzer, Joost Bastings and Stefan Riezler · 2019
Later among the works it cites.
“Dataset for comparable evaluation of machine translation between 11 South African languages”
Cindy McKellar and Martin Puttkammer · 2020
Closest in time.
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