Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

Existing work in translation demonstrated the potential of massively multilingual machine
translation by training a single model able to translate between any pair of languages …

While BERT is an effective method for learning monolingual sentence embeddings for
semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019) …

We present an easy and efficient method to extend existing sentence embedding models to
new languages. This allows to create multilingual versions from previously monolingual …

We introduce an architecture to learn joint multilingual sentence representations for 93
languages, belonging to more than 30 different families and written in 28 different scripts …

Question answering (QA) models have shown rapid progress enabled by the availability of
large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to …

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 …

What does it take to create the Babel Fish, a tool that can help individuals translate speech
between any two languages? While recent breakthroughs in text-based models have …

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 an approach based on multilingual sentence embeddings to automatically
extract parallel sentences from the content of Wikipedia articles in 85 languages, including …