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Edouard Grave
Researcher at Facebook
Publications - 98
Citations - 29279
Edouard Grave is an academic researcher from Facebook. The author has contributed to research in topics: Word (computer architecture) & Question answering. The author has an hindex of 43, co-authored 91 publications receiving 19126 citations. Previous affiliations of Edouard Grave include École Normale Supérieure & Columbia University.
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Journal ArticleDOI
Enriching Word Vectors with Subword Information
TL;DR: This paper proposed a new approach based on skip-gram model, where each word is represented as a bag of character n-grams, words being represented as the sum of these representations, allowing to train models on large corpora quickly and allowing to compute word representations for words that did not appear in the training data.
Proceedings ArticleDOI
Bag of Tricks for Efficient Text Classification
TL;DR: FastText as mentioned in this paper explores a simple and efficient baseline for text classification, which is often on par with deep learning classifiers in terms of accuracy and many orders of magnitude faster for training and evaluation.
Proceedings ArticleDOI
Unsupervised Cross-lingual Representation Learning at Scale
Alexis Conneau,Kartikay Khandelwal,Naman Goyal,Vishrav Chaudhary,Guillaume Wenzek,Francisco Guzmán,Edouard Grave,Myle Ott,Luke Zettlemoyer,Veselin Stoyanov +9 more
TL;DR: It is shown that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks, and the possibility of multilingual modeling without sacrificing per-language performance is shown for the first time.
Posted Content
Enriching Word Vectors with Subword Information
TL;DR: A new approach based on the skipgram model, where each word is represented as a bag of character n-grams, with words being represented as the sum of these representations, which achieves state-of-the-art performance on word similarity and analogy tasks.
Proceedings Article
Learning Word Vectors for 157 Languages
TL;DR: This article used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project, and introduced three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish.