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Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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The university of Edinburgh’s submissions to the WMT18 news translation task
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a
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Fon french daily dialogues parallel data
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