In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

Driven by the goal of eradicating language barriers on a global scale, machine translation
has solidified itself as a key focus of artificial intelligence research today. However, such …

We demonstrate the potential of few-shot translation systems, trained with unpaired
language data, for both high and low-resource language pairs. We show that with only 5 …

Large language models (LLMs) that have been trained on multilingual but not parallel text
exhibit a remarkable ability to translate between languages. We probe this ability in an in …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

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

This paper presents the results of the news translation task, the multilingual low-resource
translation for Indo-European languages, the triangular translation task, and the automatic …

Abstract In Neural Machine Translation, it is typically assumed that the sentence with the
highest estimated probability should also be the translation with the highest quality as …

Many engineering disciplines require problem-solving skills, which cannot be learned by
memorization alone. Open-ended textual exercises allow students to acquire these skills …