@inproceedings{wein-etal-2023-measuring,
    title = "Measuring Fine-Grained Semantic Equivalence with {A}bstract {M}eaning {R}epresentation",
    author = "Wein, Shira  and
      Wang, Zhuxin  and
      Schneider, Nathan",
    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/fix-sig-urls/2023.iwcs-1.16/",
    pages = "144--154",
    abstract = "Identifying semantically equivalent sentences is important for many NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to ``equivalence,'' despite evidence that fine-grained differences and implicit content have an effect on human understanding and system performance. In this work, we introduce a novel, more sensitive method of characterizing cross-lingual semantic equivalence that leverages Abstract Meaning Representation graph structures. We find that parsing sentences into AMRs and comparing the AMR graphs enables finer-grained equivalence measurement than comparing the sentences themselves. We demonstrate that when using gold or even automatically parsed AMR annotations, our solution is finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics."
}