MCSE: Multimodal Contrastive Learning of Sentence Embeddings

Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, Dietrich Klakow


Abstract
Learning semantically meaningful sentence embeddings is an open problem in natural language processing. In this work, we propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective. Through experiments on a variety of semantic textual similarity tasks, we demonstrate that our approach consistently improves the performance across various datasets and pre-trained encoders. In particular, combining a small amount of multimodal data with a large text-only corpus, we improve the state-of-the-art average Spearman’s correlation by 1.7%. By analyzing the properties of the textual embedding space, we show that our model excels in aligning semantically similar sentences, providing an explanation for its improved performance.
Anthology ID:
2022.naacl-main.436
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5959–5969
Language:
URL:
https://aclanthology.org/2022.naacl-main.436
DOI:
10.18653/v1/2022.naacl-main.436
Bibkey:
Cite (ACL):
Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, and Dietrich Klakow. 2022. MCSE: Multimodal Contrastive Learning of Sentence Embeddings. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5959–5969, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
MCSE: Multimodal Contrastive Learning of Sentence Embeddings (Zhang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.436.pdf
Code
 uds-lsv/mcse
Data
COCOFlickr30k