@inproceedings{flanigan-etal-2022-meaning,
    title = "Meaning Representations for Natural Languages: Design, Models and Applications",
    author = "Flanigan, Jeffrey  and
      Jindal, Ishan  and
      Li, Yunyao  and
      O{'}Gorman, Tim  and
      Palmer, Martha  and
      Xue, Nianwen",
    editor = "El-Beltagy, Samhaa R.  and
      Qiu, Xipeng",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = dec,
    year = "2022",
    address = "Abu Dubai, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-tutorials.1",
    doi = "10.18653/v1/2022.emnlp-tutorials.1",
    pages = "1--8",
    abstract = "This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with extensive experience in designing, building and using meaning representations, our tutorial has three components: (1) an introduction to common meaning representations, including basic concepts and design challenges; (2) a review of SoTA methods on building models for meaning representations; and (3) an overview of applications of meaning representations in downstream NLP tasks and real-world applications. We will also present qualitative comparisons of common meaning representations and a quantitative study on how their differences impact model performance. Finally, we will share best practices in choosing the right meaning representation for downstream tasks.",
}