@inproceedings{gonzalez-delgado-navarro-colorado-2024-simplification,
    title = "The Simplification of the Language of Public Administration: The Case of Ombudsman Institutions",
    author = "Gonzalez-Delgado, Gabriel  and
      Navarro-Colorado, Borja",
    editor = "Nunzio, Giorgio Maria Di  and
      Vezzani, Federica  and
      Ermakova, Liana  and
      Azarbonyad, Hosein  and
      Kamps, Jaap",
    booktitle = "Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.determit-1.12",
    pages = "125--133",
    abstract = "Language produced by Public Administrations has crucial implications in citizens{'} lives. However, its syntactic complexity and the use of legal jargon, among other factors, make it difficult to be understood for laypeople and certain target audiences. The NLP task of Automatic Text Simplification (ATS) can help to the necessary simplification of this technical language. For that purpose, specialized parallel datasets of complex-simple pairs need to be developed for the training of these ATS systems. In this position paper, an on-going project is presented, whose main objectives are (a) to extensively analyze the syntactical, lexical, and discursive features of the language of English-speaking ombudsmen, as samples of public administrative language, with special attention to those characteristics that pose a threat to comprehension, and (b) to develop the OmbudsCorpus, a parallel corpus of complex-simple supra-sentential fragments from ombudsmen{'}s case reports that have been manually simplified by professionals and annotated with standardized simplification operations. This research endeavor aims to provide a deeper understanding of the simplification process and to enhance the training of ATS systems specialized in administrative texts.",
}

Language produced by Public Administrations has crucial implications in citizens{'} lives. However, its syntactic complexity and the use of legal jargon, among other factors, make it difficult to be understood for laypeople and certain target audiences. The NLP task of Automatic Text Simplification (ATS) can help to the necessary simplification of this technical language. For that purpose, specialized parallel datasets of complex-simple pairs need to be developed for the training of these ATS systems. In this position paper, an on-going project is presented, whose main objectives are (a) to extensively analyze the syntactical, lexical, and discursive features of the language of English-speaking ombudsmen, as samples of public administrative language, with special attention to those characteristics that pose a threat to comprehension, and (b) to develop the OmbudsCorpus, a parallel corpus of complex-simple supra-sentential fragments from ombudsmen{'}s case reports that have been manually simplified by professionals and annotated with standardized simplification operations. This research endeavor aims to provide a deeper understanding of the simplification process and to enhance the training of ATS systems specialized in administrative texts.
Gonzalez-Delgado, Gabriel Navarro-Colorado, Borja
The Simplification of the Language of Public Administration: The Case of Ombudsman Institutions