Learning Functional Distributional Semantics with Visual DataDownload PDF

Anonymous

17 Sept 2021 (modified: 05 May 2023)ACL ARR 2021 September Blind SubmissionReaders: Everyone
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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