One of the biggest challenges hindering progress in low-resource and multilingual machine
translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either …

The development of neural techniques has opened up new avenues for research in
machine translation. Today, neural machine translation (NMT) systems can leverage highly …

We present the results of the first task on Large-Scale Multilingual Machine Translation. The
task consists on the many-to-many evaluation of a single model across a variety of source …

Modern machine translation models and language models are able to translate without
having been trained on parallel data, greatly expanding the set of languages that they can …

Task-oriented personal assistants enable people to interact with a host of devices and
services using natural language. One of the challenges of making neural dialogue systems …

Data sparsity is a main problem hindering the development of code-switching (CS) NLP
systems. In this paper, we investigate data augmentation techniques for synthesizing …

The paper discusses the implications of recent advances in artificial intelligence for
knowledge workers, focusing on possible complementarities and substitution between …

For most language combinations, parallel data is either scarce or simply unavailable. To
address this, unsupervised machine translation (UMT) exploits large amounts of …

Data augmentation techniques have been proven useful in many applications in NLP fields.
Most augmentations are task-specific, and cannot be used as a general-purpose tool. In our …

The problems of machine translation are constantly arising. While the most advanced
translation platforms, such as Google and Yandex, allow for high-quality translations of …