Unsupervised learning of sentence embeddings using compositional n-gram features

Pagliardini, Matteo; Gupta, Prakhar; Jaggi, Martin; Grave, Edouard; Bojanowski, Piotr; Gupta, Prakhar; Joulin, Armand; Mikolov, Tomas

Prakhar Gupta

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prakhar.gupta@epfl.ch +41 21 693 13 99

Nationalité : Indian

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https://scholar.google.ch/citations?user=wRJyHpIAAAAJ&hl=en&oi=ao

EPFL IC IINFCOM MLO
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Domaines de compétences

Machine Learning, Natural Language Processing and Optimization

Formation

B.Tech.	Aerospace Engineering	Indian Institute of Technology Kanpur	2010-14
M.Tech.	Computer Science and Engineering	Indian Institute of Technology Bombay	2014-16

Publications Infoscience

Unsupervised learning of sentence embeddings

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Home > Unsupervised learning of sentence embeddings using compositional n-gram features

Unsupervised learning of sentence embeddings using compositional n-gram features

Pagliardini, Matteo ; Gupta, Prakhar ; Jaggi, Martin

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NAACL 2018 - Conference of the North American Chapter of the Association for Computational Linguistics

Year:

May 01 2018

Laboratories:

MLO

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Scientific production and competences > I&C - School of Computer and Communication Sciences > IINFCOM > MLO - Machine Learning and Optimization Laboratory
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Record created 2018-05-14, last modified 2019-08-12

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Learning Word Vectors for 157 Languages

Learning Word Vectors for 157 Languages - Infoscience <script type="text/javascript" src="/js/jquery.min.js"></script> <script type="text/javascript" src="/js/tind.js?425ae572c71e1fce6a7cb4b88d683e2b"></script>

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Home > Learning Word Vectors for 157 Languages

Learning Word Vectors for 157 Languages

Grave, Edouard ; Bojanowski, Piotr ; Gupta, Prakhar ; Joulin, Armand ; Mikolov, Tomas ; Grave, Edouard ; Bojanowski, Piotr ; Joulin, Armand ; Mikolov, Tomas

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.

Presented at:

Language Resources and Evaluation Conference, Miyazaki, Japan, May 7-12, 2018

Year:

Feb 19 2018

Laboratories:

MLO

Record appears in:

Scientific production and competences > I&C - School of Computer and Communication Sciences > IINFCOM > MLO - Machine Learning and Optimization Laboratory
Work outside EPFL
Conference Papers