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Found 6 papers, 0 papers with code
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks.
A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data
Many language pairs are low resource, meaning the amount and/or quality of available parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy.
Enhanced back-translation for low resource neural machine translation using self-training
The synthetic data generated by the improved English-German backward model was used to train a forward model which out-performed another forward model trained using standard back-translation by 2. 7 BLEU.
Tag-less Back-Translation
The standard back-translation method has been shown to be unable to efficiently utilize the available huge amount of existing monolingual data because of the inability of translation models to differentiate between the authentic and synthetic parallel data during training.
Ranked #32 on
Machine Translation
on IWSLT2014 German-English
(using extra training data)
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model
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-side monolingual data.
hauWE: Hausa Words Embedding for Natural Language Processing
This work presents words embedding models using Word2Vec's Continuous Bag of Words (CBoW) and Skip Gram (SG) models.
