Learning Functional Distributional Semantics with Visual Data

• URL: http://arxiv.org/abs/2204.10624v1
• Date: Fri, 22 Apr 2022 10:41:39 GMT
• Title: Learning Functional Distributional Semantics with Visual Data
• Authors: Yinhong Liu, Guy Emerson
• Abstract summary: 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.
• Score: 12.640283469603355
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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