9 citations · 9 across the 1 of their papers we have counts for
5 papers · 1 filter
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?
Idris Abdulmumin, Bashir Shehu Galadanci, Garba Aliyu +1
Monolingual data, being readily available in large quantities, has been used to upscale the scarcely available parallel data to train better models for automatic translation. Self-…
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud +7
Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context…
Enhanced back-translation for low resource neural machine translation using self-training
Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa
Improving neural machine translation (NMT) models using the back-translations of the monolingual target data (synthetic parallel data) is currently the state-of-the-art approach fo…
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model
Idris Abdulmumin, Bashir Shehu Galadanci, Abubakar Isa
An effective method to generate a large number of parallel sentences for training improved neural machine translation (NMT) systems is the use of back-translations of the target-si…
hauWE: Hausa Words Embedding for Natural Language Processing
Idris Abdulmumin, Bashir Shehu Galadanci
Words embedding (distributed word vector representations) have become an essential component of many natural language processing (NLP) tasks such as machine translation, sentiment…