Search Results for author:
Found 5 papers, 2 papers with code
Low-resource neural machine translation with morphological modeling
An attention augmentation scheme to the transformer model is proposed in a generic form to allow integration of pre-trained language models and also facilitate modeling of word order relationships between the source and target languages.
Data Augmentation
Low-Resource Neural Machine Translation
+2
KinSPEAK: Improving speech recognition for Kinyarwanda via semi-supervised learning methods
In this work, we show that using self-supervised pre-training, following a simple curriculum schedule during fine-tuning and using semi-supervised learning to leverage large unlabelled speech data significantly improve speech recognition performance for Kinyarwanda.
KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis
This paper describes the system entered by the author to the SemEval-2023 Task 12: Sentiment analysis for African languages.
KinyaBERT: a Morphology-aware Kinyarwanda Language Model
We address these challenges by proposing a simple yet effective two-tier BERT architecture that leverages a morphological analyzer and explicitly represents morphological compositionality.
Morphological Disambiguation from Stemming Data
Kinyarwanda, a morphologically rich language, currently lacks tools for automated morphological analysis.
