@inproceedings{pirhadi-etal-2022-using,
    title = "Using Two Losses and Two Datasets Simultaneously to Improve {T}empo{W}i{C} Accuracy",
    author = "Pirhadi, Mohammad Javad  and
      Mirzaei, Motahhare  and
      Eetemadi, Sauleh",
    editor = "Barbieri, Francesco  and
      Camacho-Collados, Jose  and
      Dhingra, Bhuwan  and
      Espinosa-Anke, Luis  and
      Gribovskaya, Elena  and
      Lazaridou, Angeliki  and
      Loureiro, Daniel  and
      Neves, Leonardo",
    booktitle = "Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.evonlp-1.3/",
    doi = "10.18653/v1/2022.evonlp-1.3",
    pages = "12--15",
    abstract = "WSD (Word Sense Disambiguation) is the task of identifying which sense of a word is meant in a sentence or other segment of text. Researchers have worked on this task (e.g. Pustejovsky, 2002) for years but it{'}s still a challenging one even for SOTA (state-of-the-art) LMs (language models). The new dataset, TempoWiC introduced by Loureiro et al. (2022b) focuses on the fact that words change over time. Their best baseline achieves 70.33{\%} macro-F1. In this work, we use two different losses simultaneously. We also improve our model by using another similar dataset to generalize better. Our best configuration beats their best baseline by 4.23{\%}."
}