Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to
low-resourced languages has not yet been adequately solved." Low-resourced"-ness is a …

Language pairs with limited amounts of parallel data, also known as low-resource
languages, remain a challenge for neural machine translation. While the Transformer model …

A shellcode is a small piece of code and it is executed to exploit a software vulnerability,
which allows the target computer to execute arbitrary commands from the attacker through a …

A" bigger is better" explosion in the number of parameters in deep neural networks has
made it increasingly challenging to make state-of-the-art networks accessible in compute …

Low-resource language translation is a challenging but socially valuable NLP task. Building
on recent work adapting the Transformer's normalization to this setting, we propose …

Semantic parsing using sequence-to-sequence models allows parsing of deeper
representations compared to traditional word tagging based models. In spite of these …

The Transformer model is the state-of-the-art in Machine Translation. However, in general,
neural translation models often under perform on language pairs with insufficient training …

In this study, a human evaluation is carried out on how hyperparameter settings impact the
quality of Transformer-based Neural Machine Translation (NMT) for the low-resourced …

We study the role of an essential hyper-parameter that governs the training of Transformers
for neural machine translation in a low-resource setting: the batch size. Using theoretical …

Research into machine translation for African languages is very limited and low-resourced in
terms of datasets and model evaluations. This work aims to add to the field of neural …