Maha Elbayad

TL;DR: A conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages is developed, laying important groundwork towards realizing a universal translation system.

TL;DR: This work proposes an alternative approach which instead relies on a single 2D convolutional neural network across both sequences, which outperforms state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.

TL;DR: For each shared task of the 19th International Conference on Spoken Language Translation, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved are detailed.

TL;DR: The authors train Transformer models which can make output predictions at different stages of the network and investigate different ways to predict how much computation is required for a particular sequence, which can adjust both the amount of computation as well as the model capacity.

TL;DR: This paper trains Transformer models which can make output predictions at different stages of the network and investigates different ways to predict how much computation is required for a particular sequence.