@inproceedings{lo-etal-2025-discoclip,
    title = "{D}is{C}o{CLIP}: A Distributional Compositional Tensor Network Encoder for Vision-Language Understanding",
    author = {Lo, Kin Ian  and
      Hawashin, Hala  and
      Abbaszadeh, Mina  and
      Limb{\"a}ck-Stokin, Tilen Gaetano  and
      Wazni, Hadi  and
      Sadrzadeh, Mehrnoosh},
    editor = "Frermann, Lea  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.starsem-1.25/",
    pages = "316--327",
    ISBN = "979-8-89176-340-1",
    abstract = "Recent vision{--}language models excel at large-scale image{--}text alignment but often neglect the compositional structure of language, leading to failures on tasks that hinge on word order and predicate{--}argument structure. We introduce DisCoCLIP, a multimodal encoder that combines a frozen CLIP vision transformer with a novel tensor network text encoder that explicitly encodes syntactic structure. Sentences are parsed with a Combinatory Categorial Grammar parser to yield distributional word tensors whose contractions mirror the sentence{'}s grammatical derivation. To keep the model efficient, high-order tensors are factorized with tensor decompositions, reducing parameter count from tens of millions to under one million. Trained end-to-end with a self-supervised contrastive loss, DisCoCLIP markedly improves sensitivity to verb semantics and word order: it raises CLIP{'}s SVO-Probes verb accuracy from 77.6{\%} to 82.4{\%}, boosts ARO attribution and relation scores by over 9{\%} and 4{\%}, and achieves 93.7{\%} on a newly introduced SVO-Swap benchmark. These results demonstrate that embedding explicit linguistic structure via tensor networks yields interpretable, parameter-efficient representations that substantially improve compositional reasoning in vision{--}language tasks."
}