@inproceedings{church-etal-2022-gentle,
    title = "A Gentle Introduction to Deep Nets and Opportunities for the Future",
    author = "Church, Kenneth  and
      Kordoni, Valia  and
      Marcus, Gary  and
      Davis, Ernest  and
      Ma, Yanjun  and
      Chen, Zeyu",
    editor = "Benotti, Luciana  and
      Okazaki, Naoaki  and
      Scherrer, Yves  and
      Zampieri, Marcos",
    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.1",
    doi = "10.18653/v1/2022.acl-tutorials.1",
    pages = "1--6",
    abstract = "The first half of this tutorial will make deep nets more accessible to a broader audience, following {``}Deep Nets for Poets{''} and {``}A Gentle Introduction to Fine-Tuning.{''} We will also introduce GFT (general fine tuning), a little language for fine tuning deep nets with short (one line) programs that are as easy to code as regression in statistics packages such as R using glm (general linear models). Based on the success of these methods on a number of benchmarks, one might come away with the impression that deep nets are all we need. However, we believe the glass is half-full: while there is much that can be done with deep nets, there is always more to do. The second half of this tutorial will discuss some of these opportunities.",
}