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Shaping representations through communication: community size effect in artificial learning systems ...
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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input ...
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations ...
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The LAMBADA dataset: Word prediction requiring a broad discourse context ...
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Combining Language and Vision with a Multimodal Skip-gram Model ...
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Improving zero-shot learning by mitigating the hubness problem ...
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Improving zero-shot learning by mitigating the hubness problem ...
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From Visual Attributes to Adjectives through Decompositional Distributional Semantics ...
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C-PHRASE Vectors ...
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Abstract:
The C-PHRASE semantic vectors described in the following paper: N.T. Pham, G. Kruszewski, A. Lazaridou, M. Baroni Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model. Proceedings of ACL 2015 (53rd Annual Meeting of the Association for Computational Linguistics). The vectors are the rows of a dense matrix stored in a text file using the tab-delimited format (the first element of each line corresponds to a word, followed by the values in the vector representing it). The text file is then compressed and split it into two files to make the download easier. Please download the following parts, and then concatenate them as shown below: cphrase.txt.zip_aa cphrase.txt.zip_ab cat cphrase.txt.zip_* > cphrase.txt.zip ...
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Keyword:
computational linguistics, entailment, sentence similarity, sentence relatedness, compositional semantics, distributional semantics
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URL: https://zenodo.org/record/2788100 https://dx.doi.org/10.5281/zenodo.2788100
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Improving zero-shot learning by mitigating the hubness problem ...
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Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model
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“Look, some green circles!”: learning to quantify from images
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"The red one!": on learning to refer to things based on discriminative properties
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