@inproceedings{dary-nasr-2021-reading,
    title = "The Reading Machine: A Versatile Framework for Studying Incremental Parsing Strategies",
    author = "Dary, Franck  and
      Nasr, Alexis",
    editor = "Oepen, Stephan  and
      Sagae, Kenji  and
      Tsarfaty, Reut  and
      Bouma, Gosse  and
      Seddah, Djam{\'e}  and
      Zeman, Daniel",
    booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.iwpt-1.3/",
    doi = "10.18653/v1/2021.iwpt-1.3",
    pages = "26--37",
    abstract = "The Reading Machine, is a parsing framework that takes as input raw text and performs six standard nlp tasks: tokenization, pos tagging, morphological analysis, lemmatization, dependency parsing and sentence segmentation. It is built upon Transition Based Parsing, and allows to implement a large number of parsing configurations, among which a fully incremental one. Three case studies are presented to highlight the versatility of the framework. The first one explores whether an incremental parser is able to take into account top-down dependencies (i.e. the influence of high level decisions on low level ones), the second compares the performances of an incremental and a pipe-line architecture and the third quantifies the impact of the right context on the predictions made by an incremental parser."
}