Neural Variational Learning for Grounded Language Acquisition

• URL: http://arxiv.org/abs/2107.14593v1
• Date: Tue, 20 Jul 2021 20:55:02 GMT
• Title: Neural Variational Learning for Grounded Language Acquisition
• Authors: Nisha Pillai, Cynthia Matuszek, Francis Ferraro
• Abstract summary: We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We show that this generative approach exhibits promising results in language grounding without pre-specifying visual categories under low resource settings.
• Score: 14.567067583556714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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