@inproceedings{jeawak-etal-2020-cardiff,
    title = "{C}ardiff {U}niversity at {S}em{E}val-2020 Task 6: Fine-tuning {BERT} for Domain-Specific Definition Classification",
    author = "Jeawak, Shelan  and
      Espinosa-Anke, Luis  and
      Schockaert, Steven",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.semeval-1.44/",
    doi = "10.18653/v1/2020.semeval-1.44",
    pages = "361--366",
    abstract = "We describe the system submitted to SemEval-2020 Task 6, Subtask 1. The aim of this subtask is to predict whether a given sentence contains a definition or not. Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained BERT language model. In this paper, we analyze the performance of this strategy. Among others, we show that results can be improved by using a two-step fine-tuning process, in which the BERT model is first fine-tuned on the full training set, and then further specialized towards a target domain."
}