@inproceedings{bertolini-etal-2023-towards,
    title = "Towards Unsupervised Compositional Entailment with Multi-Graph Embedding Models",
    author = "Bertolini, Lorenzo  and
      Weeds, Julie  and
      Weir, David",
    editor = "Amblard, Maxime  and
      Breitholtz, Ellen",
    booktitle = "Proceedings of the 15th International Conference on Computational Semantics",
    month = jun,
    year = "2023",
    address = "Nancy, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.iwcs-1.5/",
    pages = "50--61",
    abstract = "Compositionality and inference are essential features of human language, and should hence be simultaneously accessible to a model of meaning. Despite being theory-grounded, distributional models can only be directly tested on compositionality, usually through similarity judgements, while testing for inference requires external resources. Recent work has shown that knowledge graph embeddings (KGE) architectures can be used to train distributional models capable of learning syntax-aware compositional representations, by training on syntactic graphs. We propose to expand such work with Multi-Graphs embedding (MuG) models, a new set of models learning from syntactic and knowledge-graphs. Using a phrase-level inference task, we show how MuGs can simultaneously handle syntax-aware composition and inference, and remain competitive distributional models with respect to lexical and compositional similarity."
}