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arXiv · 1712.09405

Advances in Pre-Training Distributed Word Representations

Tomas Mikolov·Edouard Grave·Piotr Bojanowski·Christian Puhrsch·Armand Joulin
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Abstract

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.

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Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin. 2017-12-26. Advances in Pre-Training Distributed Word Representations. https://arxiv.org/abs/1712.09405

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