@inproceedings{chu-etal-2022-hit,
    title = "{HIT} at {S}em{E}val-2022 Task 2: Pre-trained Language Model for Idioms Detection",
    author = "Chu, Zheng  and
      Yang, Ziqing  and
      Cui, Yiming  and
      Chen, Zhigang  and
      Liu, Ming",
    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/add-emnlp-2024-awards/2022.semeval-1.28/",
    doi = "10.18653/v1/2022.semeval-1.28",
    pages = "221--227",
    abstract = "The same multi-word expressions may have different meanings in different sentences. They can be mainly divided into two categories, which are literal meaning and idiomatic meaning. Non-contextual-based methods perform poorly on this problem, and we need contextual embedding to understand the idiomatic meaning of multi-word expressions correctly. We use a pre-trained language model, which can provide a context-aware sentence embedding, to detect whether multi-word expression in the sentence is idiomatic usage."
}