  Many Natural Language Processing applications nowadays rely on pre-trained
word representations estimated from large text corpora such as news
collections, Wikipedia and Web Crawl. In this paper, we show how to train
high-quality word vector representations by using a combination of known tricks
that are however rarely used together. The main result of our work is the new
set of publicly available pre-trained models that outperform the current state
of the art by a large margin on a number of tasks.
