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What you can cram into a single \$&!#* vector: Probing sentence embeddings for linguistic properties
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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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What you can cram into a single vector: Probing sentence embeddings for linguistic properties ...
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The LAMBADA dataset: Word prediction requiring a broad discourse context ...
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Generation for Grammar Engineering
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In: Proceedings of the seventh International Natural Language Generation Conference ; INLG 2012, The seventh International Natural Language Generation Conference. ; https://hal.archives-ouvertes.fr/hal-00768612 ; INLG 2012, The seventh International Natural Language Generation Conference., May 2012, Starved Rock, Illinois, United States. pp.31-40 (2012)
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Generating Grammar Exercises
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In: Proceeding of the 7th Workshop on Innovative Use of NLP for Building Educational Applications, NAACL-HLT Worskhop 2012 ; The 7th Workshop on Innovative Use of NLP for Building Educational Applications, NAACL-HLT Worskhop 2012 ; https://hal.archives-ouvertes.fr/hal-00768610 ; The 7th Workshop on Innovative Use of NLP for Building Educational Applications, NAACL-HLT Worskhop 2012, Jun 2012, Montreal, Canada. pp.147-157 (2012)
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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
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What you can cram into a single $&!#* vector: probing sentence embeddings for linguistic properties
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There is no logical negation here, but there are alternatives: modeling conversational negation with distributional semantics
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The LAMBADA dataset: word prediction requiring a broad discourse context
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