4 accepted papers
Achieving robust speech recognition for Kinyarwanda remains a challenging task. In this work, we empirically show that using self-supervised pretraining, following a curriculum schedule during fine-tuning and using semi-supervised learning improve speech recognition for Kinyarwanda. Our approach foc…
Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation for morphologically-rich languages. However, existing methods such as sub-word tokenization and character-based models are limited to the surface forms of the words. In…
Pre-trained language models such as BERT have been successful at tackling many natural language processing tasks. However, the unsupervised sub-word tokenization methods commonly used in these models (e.g., byte-pair encoding - BPE) are sub-optimal at handling morphologically rich languages. Even gi…
Morphological analysis and disambiguation is an important task and a crucial preprocessing step in natural language processing of morphologically rich languages. Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis. While linguistically curated fin…