@inproceedings{zhang-etal-2022-mcse,
    title = "{MCSE}: {M}ultimodal Contrastive Learning of Sentence Embeddings",
    author = "Zhang, Miaoran  and
      Mosbach, Marius  and
      Adelani, David Ifeoluwa  and
      Hedderich, Michael A.  and
      Klakow, Dietrich",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2022.naacl-main.436/",
    doi = "10.18653/v1/2022.naacl-main.436",
    pages = "5959--5969",
    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."
}