@inproceedings{kuimov-etal-2022-skoltechnlp,
    title = "{S}koltech{NLP} at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations",
    author = "Kuimov, Mikhail  and
      Dementieva, Daryna  and
      Panchenko, Alexander",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.semeval-1.160/",
    doi = "10.18653/v1/2022.semeval-1.160",
    pages = "1136--1144",
    abstract = "This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we{'}ve considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation). The results prove that Transformer models produce the best scores. However, there is space for research and approaches that give not yet comparable but more interpretable results."
}