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- W2795877110 abstract "Yoruba is a widely spoken West African language with a writing system rich in tonal and orthographic diacritics. With very few exceptions, diacritics are omitted from electronic texts, due to limited device and application support. Diacritics provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any Yoruba text-to-speech (TTS), automatic speech recognition (ASR) and natural language processing (NLP) tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation task, we experiment with two different attentive Sequence-to-Sequence neural models to process undiacritized text. On our evaluation dataset, this approach produces diacritization error rates of less than 5%. We have released pre-trained models, datasets and source-code as an open-source project to advance efforts on Yoruba language technology." @default.
- W2795877110 created "2018-04-13" @default.
- W2795877110 creator A5016069022 @default.
- W2795877110 date "2018-04-03" @default.
- W2795877110 modified "2023-09-27" @default.
- W2795877110 title "Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yor`ub'a Language Text." @default.
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