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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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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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Abstract:
Comunicació presentada a: 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing celebrat del 26 al 31 de juliol de 2015 a Pequín, Xina. ; We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full sentences. C-PHRASE outperforms the state-of-theart C-BOW model on a variety of lexical tasks. Moreover, since C-PHRASE word vectors are induced through a compositional learning objective (modeling the contexts of words combined into phrases), when they are summed, they produce sentence representations that rival those generated by ad-hoc compositional models. ; We thank Gemma Boleda and the anonymous reviewers for useful comments. We acknowledge ERC 2011 Starting Independent Research Grant n. 283554 (COMPOSES).
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URL: http://hdl.handle.net/10230/46044 https://doi.org/10.3115/v1/P15-1094
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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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