Learning Functional Distributional Semantics with Visual Data
Yinhong Liu, Guy Emerson · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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. 1