MCSE: Multimodal Contrastive Learning of Sentence Embeddings

• URL: http://arxiv.org/abs/2204.10931v1
• Date: Fri, 22 Apr 2022 21:19:24 GMT
• Title: MCSE: Multimodal Contrastive Learning of Sentence Embeddings
• Authors: Miaoran Zhang, Marius Mosbach, David Ifeoluwa Adelani, Michael A. Hedderich, Dietrich Klakow
• Abstract summary: We propose a sentence embedding learning approach that exploits both visual and textual information via a multimodal contrastive objective. We show that our approach consistently improves the performance across various datasets and pre-trained encoders.
• Score: 23.630041603311923
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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