Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text
This work addresses a critical issue for Yorùbá language technology, enabling improved text-to-speech, speech recognition, and NLP tasks by restoring omitted diacritics in electronic texts.
The paper tackled the problem of automatic diacritic restoration for the Yorùbá language by reframing it as a machine translation task using attentive sequence-to-sequence neural models, achieving a diacritization error rate of less than 5% on their evaluation dataset.
Yorùbá 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 Yorùbá 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 Yorùbá language technology.