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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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Abstract:
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM) build vector-based word representations by learning to predict linguistic contexts in text corpora. However, for a restricted set of words, the models are also exposed to visual representations of the objects they denote (extracted from natural images), and must predict linguistic and visual features jointly. The MMSKIP-GRAM models achieve good performance on a variety of semantic benchmarks. Moreover, since they propagate visual information to all words, we use them to improve image labeling and retrieval in the zero-shot setup, where the test concepts are never seen during model training. Finally, the MMSKIP-GRAM models discover intriguing visual properties of abstract words, paving the way to realistic implementations of embodied theories of meaning. ... : accepted at NAACL 2015, camera ready version, 11 pages ...
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Keyword:
Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1501.02598 https://arxiv.org/abs/1501.02598
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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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“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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