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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 ...
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Paperno, Denis; Kruszewski, Germán; Lazaridou, Angeliki; Pham, Quan Ngoc; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Boleda, Gemma; Fernández, Raquel. - : Zenodo, 2016
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Abstract:
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text. The LAMBADA paper can be found here. ...
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URL: https://dx.doi.org/10.5281/zenodo.2630550
https://zenodo.org/record/2630550
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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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