Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

The aim of this paper is to give an overview of domain adaptation and transfer learning with
a specific view on visual applications. After a general motivation, we first position domain …

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 …

Recent studies have demonstrated the efficiency of generative pretraining for English
natural language understanding. In this work, we extend this approach to multiple …

State-of-the-art natural language processing systems rely on supervision in the form of
annotated data to learn competent models. These models are generally trained on data in a …

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 …

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

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

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 …