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arXiv · 2204.10624

Learning Functional Distributional Semantics with Visual Data

Yinhong Liu·Guy Emerson
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Abstract

Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics model with grounded visual data. We train it on the Visual Genome dataset, which is closer to the kind of data encountered in human language acquisition than a large text corpus. On four external evaluation datasets, our model outperforms previous work on learning semantics from Visual Genome.

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Yinhong Liu, Guy Emerson. 2022-04-22. Learning Functional Distributional Semantics with Visual Data. https://arxiv.org/abs/2204.10624

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