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Hits 1 – 9 of 9
1
What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
Conneau, Alexis
;
Kruszewski, German
;
Lample, Guillaume
...
In: ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01898412 ; ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, Australia. pp.2126-2136 (2018)
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2
XNLI: Evaluating Cross-lingual Sentence Representations ...
Conneau, Alexis
;
Lample, Guillaume
;
Rinott, Ruty
. - : arXiv, 2018
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3
What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
Conneau, Alexis
;
Kruszewski, German
;
Lample, Guillaume
. - : arXiv, 2018
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4
Fader Networks: Manipulating Images by Sliding Attributes
Lample, Guillaume
;
Zeghidour, Neil
;
Usunier, Nicolas
...
In: 31st Conference on Neural Information Processing Systems (NIPS 2017) ; https://hal.archives-ouvertes.fr/hal-02275215 ; 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec 2017, Long Beach, CA, United States. pp.5969-5978 (2017)
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5
Word Translation Without Parallel Data ...
Conneau, Alexis
;
Lample, Guillaume
;
Ranzato, Marc'Aurelio
. - : arXiv, 2017
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6
Massively Multilingual Word Embeddings ...
Ammar, Waleed
;
Mulcaire, George
;
Tsvetkov, Yulia
. - : arXiv, 2016
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7
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ...
Tsvetkov, Yulia
;
Sitaram, Sunayana
;
Faruqui, Manaal
. - : arXiv, 2016
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8
What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties
Kruszewski, German
;
Barrault, Loïc
;
Baroni, Marco
;
Conneau, Alexis
;
Lample, Guillaume
. - : ACL (Association for Computational Linguistics)
Abstract:
Comunicació presentada a: 56th Annual Meeting of the Association for Computational Linguistics celebrat del 15 al 20 de juliol de 2018 a Melbourne, Australia. ; Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. “Downstream” tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.
URL:
http://hdl.handle.net/10230/45965
https://doi.org/10.18653/v1/P18-1198
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9
Neural architectures for named entity recognition
Kawakami, Kazuya
;
Ballesteros, Miguel
;
Lample, Guillaume
. - : ACL (Association for Computational Linguistics)
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