@inproceedings{haim-meirom-bobrowski-2022-unsupervised,
    title = "Unsupervised Geometric and Topological Approaches for Cross-Lingual Sentence Representation and Comparison",
    author = "Haim Meirom, Shaked  and
      Bobrowski, Omer",
    editor = "Gella, Spandana  and
      He, He  and
      Majumder, Bodhisattwa Prasad  and
      Can, Burcu  and
      Giunchiglia, Eleonora  and
      Cahyawijaya, Samuel  and
      Min, Sewon  and
      Mozes, Maximilian  and
      Li, Xiang Lorraine  and
      Augenstein, Isabelle  and
      Rogers, Anna  and
      Cho, Kyunghyun  and
      Grefenstette, Edward  and
      Rimell, Laura  and
      Dyer, Chris",
    booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.repl4nlp-1.18/",
    doi = "10.18653/v1/2022.repl4nlp-1.18",
    pages = "173--183",
    abstract = "We propose novel structural-based approaches for the generation and comparison of cross lingual sentence representations. We do so by applying geometric and topological methods to analyze the structure of sentences, as captured by their word embeddings. The key properties of our methods are{\textquotedblright}:{\textquotedblright} (a) They are designed to be isometric invariant, in order to provide language-agnostic representations. (b) They are fully unsupervised, and use no cross-lingual signal. The quality of our representations, and their preservation across languages, are evaluated in similarity comparison tasks, achieving competitive results. Furthermore, we show that our structural-based representations can be combined with existing methods for improved results."
}