This article provides an overview of the methods used for algorithmic text analysis in
economics, with a focus on three key contributions. First, we introduce methods for …

We present a survey on multilingual neural machine translation (MNMT), which has gained
a lot of traction in recent years. MNMT has been useful in improving translation quality as a …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

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 …

We present COMET, a neural framework for training multilingual machine translation
evaluation models which obtains new state-of-the-art levels of correlation with human …

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

We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of
unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised …

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 …

Much recent progress in applications of machine learning models to NLP has been driven
by benchmarks that evaluate models across a wide variety of tasks. However, these broad …

Language technologies contribute to promoting multilingualism and linguistic diversity
around the world. However, only a very small number of the over 7000 languages of the …