@inproceedings{pisarevskaya-zubiaga-2022-team,
    title = "Team dina at {S}em{E}val-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity",
    author = "Pisarevskaya, Dina  and
      Zubiaga, Arkaitz",
    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/jlcl-multiple-ingestion/2022.semeval-1.169/",
    doi = "10.18653/v1/2022.semeval-1.169",
    pages = "1196--1201",
    abstract = "This paper describes the participation of the team {\textquotedblleft}dina{\textquotedblright} in the Multilingual News Similarity task at SemEval 2022. To build our system for the task, we experimented with several multilingual language models which were originally pre-trained for semantic similarity but were not further fine-tuned. We use these models in combination with state-of-the-art packages for machine translation and named entity recognition with the expectation of providing valuable input to the model. Our work assesses the applicability of such {\textquotedblleft}pure{\textquotedblright} models to solve the multilingual semantic similarity task in the case of news articles. Our best model achieved a score of 0.511, but shows that there is room for improvement."
}