ACL 2022long2 citations
Learning Functional Distributional Semantics with Visual Data
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.
BibTeX
@inproceedings{liu-emerson-2022-learning,
title = "Learning Functional Distributional Semantics with Visual Data",
author = "Liu, Yinhong and
Emerson, Guy",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.275/",
doi = "10.18653/v1/2022.acl-long.275",
pages = "3976--3988"
}