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Neural Variational Learning for Grounded Language Acquisition ...
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Abstract:
2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) Vancouver, BC, Canada 8-12 Aug. 2021 ... : We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning of language about a wide range of real-world objects. We evaluate the efficacy of this learning by predicting the semantics of objects and comparing the performance with neural and non-neural inputs. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings. Our experiments demonstrate that this approach is generalizable to multilingual, highly varied datasets. ...
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URL: https://mdsoar.org/handle/11603/24609 https://dx.doi.org/10.13016/m2hn7g-isy7
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Measuring Perceptual and Linguistic Complexity in Multilingual Grounded Language Data ...
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Neural Variational Learning for Grounded Language Acquisition ...
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Neural Variational Learning for Grounded Language Acquisition ...
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Sampling Approach Matters: Active Learning for Robotic Language Acquisition ...
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Sampling Approach Matters: Active Learning for Robotic Language Acquisition ...
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Learning Natural Language from Probabilistic Perceptual Representations with Limited Resources ...
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Building Language-Agnostic Grounded Language Learning Systems ...
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Building Language-Agnostic Grounded Language Learning Systems ...
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Deep Learning for Category-Free Grounded Language Acquisition ...
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Building Language-Agnostic Grounded Language Learning Systems ...
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Optimal Semantic Distance for Negative Example Selection in Grounded Language Acquisition ...
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Unsupervised Selection of Negative Examples for Grounded Language Learning ...
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Identifying Negative Exemplars in Grounded Language Data Sets ...
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Improving Grounded Language Acquisition Efficiency Using Interactive Labeling ...
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