@inproceedings{golchin-etal-2023-mask,
    title = "Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords",
    author = "Golchin, Shahriar  and
      Surdeanu, Mihai  and
      Tavabi, Nazgol  and
      Kiapour, Ata",
    editor = "Can, Burcu  and
      Mozes, Maximilian  and
      Cahyawijaya, Samuel  and
      Saphra, Naomi  and
      Kassner, Nora  and
      Ravfogel, Shauli  and
      Ravichander, Abhilasha  and
      Zhao, Chen  and
      Augenstein, Isabelle  and
      Rogers, Anna  and
      Cho, Kyunghyun  and
      Grefenstette, Edward  and
      Voita, Lena",
    booktitle = "Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.repl4nlp-1.2",
    doi = "10.18653/v1/2023.repl4nlp-1.2",
    pages = "13--21",
    abstract = "We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: three datasets combined with two distinct pre-trained language models (PLMs). Our results reveal that the fine-tuned PLMs adapted using our in-domain pre-training strategy outperform PLMs that used in-domain pre-training with random masking as well as those that followed the common pre-train-then-fine-tune paradigm. Further, the overhead of identifying in-domain keywords is reasonable, e.g., 7-15{\%} of the pre-training time (for two epochs) for BERT Large (Devlin et al., 2019).",
}

We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: three datasets combined with two distinct pre-trained language models (PLMs). Our results reveal that the fine-tuned PLMs adapted using our in-domain pre-training strategy outperform PLMs that used in-domain pre-training with random masking as well as those that followed the common pre-train-then-fine-tune paradigm. Further, the overhead of identifying in-domain keywords is reasonable, e.g., 7-15{\%} of the pre-training time (for two epochs) for BERT Large (Devlin et al., 2019).
Golchin, Shahriar Surdeanu, Mihai Tavabi, Nazgol Kiapour, Ata
Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords