@inproceedings{cai-etal-2022-retrofitting,
    title = "Retrofitting Multilingual Sentence Embeddings with {A}bstract {M}eaning {R}epresentation",
    author = "Cai, Deng  and
      Li, Xin  and
      Ho, Jackie Chun-Sing  and
      Bing, Lidong  and
      Lam, Wai",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.emnlp-main.433/",
    doi = "10.18653/v1/2022.emnlp-main.433",
    pages = "6456--6472",
    abstract = "We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. It also helps reduce the surface variations across different expressions and languages. Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks."
}