@inproceedings{xu-etal-2020-dombert,
    title = "{D}om{BERT}: Domain-oriented Language Model for Aspect-based Sentiment Analysis",
    author = "Xu, Hu  and
      Liu, Bing  and
      Shu, Lei  and
      Yu, Philip",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.findings-emnlp.156/",
    doi = "10.18653/v1/2020.findings-emnlp.156",
    pages = "1725--1731",
    abstract = "This paper focuses on learning domain-oriented language models driven by end tasks, which aims to combine the worlds of both general-purpose language models (such as ELMo and BERT) and domain-specific language understanding. We propose DomBERT, an extension of BERT to learn from both in-domain corpus and relevant domain corpora. This helps in learning domain language models with low-resources. Experiments are conducted on an assortment of tasks in aspect-based sentiment analysis (ABSA), demonstrating promising results."
}