@inproceedings{lee-etal-2020-pushing,
    title = "Pushing the Limits of {AMR} Parsing with Self-Learning",
    author = "Lee, Young-Suk  and
      Fernandez Astudillo, Ram{\'o}n  and
      Naseem, Tahira  and
      Gangi Reddy, Revanth  and
      Florian, Radu  and
      Roukos, Salim",
    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/jlcl-multiple-ingestion/2020.findings-emnlp.288/",
    doi = "10.18653/v1/2020.findings-emnlp.288",
    pages = "3208--3214",
    abstract = "Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0."
}