@inproceedings{poswiata-2022-opi,
    title = "{OPI} at {S}em{E}val-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment Analysis",
    author = "Po{\'s}wiata, Rafa{\l}",
    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-1.190/",
    doi = "10.18653/v1/2022.semeval-1.190",
    pages = "1366--1372",
    abstract = "This paper presents our solution for SemEval-2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules: the first for sequence tagging and the second for relation classification. In both modules we used transformer-based language models. In addition to utilizing language models specific to each of the five competition languages, we also adopted multilingual models. This approach allowed us to apply the solution to both monolingual and cross-lingual sub-tasks, where we obtained average Sentiment Graph F1 of 54.5{\%} and 53.1{\%}, respectively. The source code of the prepared solution is available at \url{https://github.com/rafalposwiata/structured-sentiment-analysis}."
}