@inproceedings{xia-etal-2022-lingjing,
    title = "{L}ing{J}ing at {S}em{E}val-2022 Task 3: Applying {D}e{BERT}a to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer",
    author = "Xia, Fei  and
      Li, Bin  and
      Weng, Yixuan  and
      He, Shizhu  and
      Sun, Bin  and
      Li, Shutao  and
      Liu, Kang  and
      Zhao, Jun",
    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://aclanthology.org/2022.semeval-1.30",
    doi = "10.18653/v1/2022.semeval-1.30",
    pages = "239--246",
    abstract = "This paper presents the results and main findings of our system on SemEval-2022 Task 3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS). This task aims at semantic competence with specific attention on the evaluation of language models, which is a task with respect to the recognition of appropriate taxonomic relations between two nominal arguments. Two sub-tasks including binary classification and regression are designed for the evaluation. For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages. Due to the small size of the training datasets of the regression sub-task, we transfer the knowledge of classification model (i.e., model parameters) to the regression task. The experimental results show that the proposed method achieves the best results on both sub-tasks. Meanwhile, we also report negative results of multiple training strategies for further discussion. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.",
}