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.- Anthology ID:
- 2022.acl-long.275
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3976–3988
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.275
- DOI:
- 10.18653/v1/2022.acl-long.275
- Cite (ACL):
- Yinhong Liu and Guy Emerson. 2022. Learning Functional Distributional Semantics with Visual Data. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3976–3988, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Learning Functional Distributional Semantics with Visual Data (Liu & Emerson, ACL 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.acl-long.275.pdf
- Data
- Visual Question Answering