@inproceedings{zhu-etal-2022-knowledge,
    title = "Knowledge-Augmented Methods for Natural Language Processing",
    author = "Zhu, Chenguang  and
      Xu, Yichong  and
      Ren, Xiang  and
      Lin, Bill Yuchen  and
      Jiang, Meng  and
      Yu, Wenhao",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-tutorials.3",
    doi = "10.18653/v1/2022.acl-tutorials.3",
    pages = "12--20",
    abstract = "Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models. NLP models with attention to knowledge can i) access unlimited amount of external information; ii) delegate the task of storing knowledge from its parameter space to knowledge sources; iii) obtain up-to-date information; iv) make prediction results more explainable via selected knowledge. In this tutorial, we will introduce the key steps in integrating knowledge into NLP, including knowledge grounding from text, knowledge representation and fusing. In addition, we will introduce recent state-of-the-art applications in fusing knowledge into language understanding, language generation and commonsense reasoning.",
}