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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search 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) BASE Show details 2 XNLI: Evaluating Cross-lingual Sentence Representations ... Conneau, Alexis; Lample, Guillaume; Rinott, Ruty; Williams, Adina; Bowman, Samuel R.; Schwenk, Holger; Stoyanov, Veselin. - : arXiv, 2018 Abstract: State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in cross-lingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned ... : EMNLP 2018 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1809.05053 https://arxiv.org/abs/1809.05053 BASE Hide details 3 What you can cram into a single vector: Probing sentence embeddings for linguistic properties ... Conneau, Alexis; Kruszewski, German; Lample, Guillaume. - : arXiv, 2018 BASE Show details 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) BASE Show details 5 Word Translation Without Parallel Data ... Conneau, Alexis; Lample, Guillaume; Ranzato, Marc'Aurelio. - : arXiv, 2017 BASE Show details 6 Massively Multilingual Word Embeddings ... Ammar, Waleed; Mulcaire, George; Tsvetkov, Yulia. - : arXiv, 2016 BASE Show details 7 Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning ... Tsvetkov, Yulia; Sitaram, Sunayana; Faruqui, Manaal. - : arXiv, 2016 BASE Show details 8 What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties Kruszewski, German; Barrault, Loïc; Baroni, Marco. - : ACL (Association for Computational Linguistics) BASE Show details 9 Neural architectures for named entity recognition Kawakami, Kazuya; Ballesteros, Miguel; Lample, Guillaume. - : ACL (Association for Computational Linguistics) BASE Show details

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