@inproceedings{zhestiankin-ponomareva-2021-zhestyatsky,
    title = "Zhestyatsky at {S}em{E}val-2021 Task 2: {R}e{LU} over Cosine Similarity for {BERT} Fine-tuning",
    author = "Zhestiankin, Boris  and
      Ponomareva, Maria",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.semeval-1.17/",
    doi = "10.18653/v1/2021.semeval-1.17",
    pages = "163--168",
    abstract = "This paper presents our contribution to SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). Our experiments cover English (EN-EN) sub-track from the multilingual setting of the task. We experiment with several pre-trained language models and investigate an impact of different top-layers on fine-tuning. We find the combination of Cosine Similarity and ReLU activation leading to the most effective fine-tuning procedure. Our best model results in accuracy 92.7{\%}, which is the fourth-best score in EN-EN sub-track."
}