@inproceedings{xu-etal-2022-hfl,
    title = "{HFL} at {S}em{E}val-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity",
    author = "Xu, Zihang  and
      Yang, Ziqing  and
      Cui, Yiming  and
      Chen, Zhigang",
    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/fix-sig-urls/2022.semeval-1.157/",
    doi = "10.18653/v1/2022.semeval-1.157",
    pages = "1114--1120",
    abstract = "This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity. We proposed a linguistics-inspired model trained with a few task-specific strategies. The main techniques of our system are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples reconstruction with the head-tail combination. We also present a brief analysis of some negative methods like two-tower architecture. Our system ranked 1st on the leaderboard while achieving a Pearson{'}s Correlation Coefficient of 0.818 on the official evaluation set."
}