Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Abstract Machine translation is an important and challenging task that aims at automatically
translating natural language sentences from one language into another. Recently …

State-of-the-art unsupervised multilingual models (eg, multilingual BERT) have been shown
to generalize in a zero-shot cross-lingual setting. This generalization ability has been …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

In spite of the recent success of neural machine translation (NMT) in standard benchmarks,
the lack of large parallel corpora poses a major practical problem for many language pairs …

Recent work has managed to learn cross-lingual word embeddings without parallel data by
mapping monolingual embeddings to a shared space through adversarial training …

State-of-the-art methods for learning cross-lingual word embeddings have relied on
bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when developing …

We study the problem of multilingual masked language modeling, ie the training of a single
model on concatenated text from multiple languages, and present a detailed study of several …

Cross-lingual or cross-domain correspondences play key roles in tasks ranging from
machine translation to transfer learning. Recently, purely unsupervised methods operating …