@inproceedings{daoudi-etal-2025-explicit,
    title = "Explicit Edge Length Coding to Improve Long Sentence Parsing Performance",
    author = "Daoudi, Khensa  and
      Dehouck, Mathieu  and
      Ziane, Rayan  and
      Romanova, Natasha",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.lowresnlp-1.11/",
    pages = "102--110",
    abstract = "Performance of syntactic parsers is reduced for longer sentences. While some of this reduction can be explained by the tendency of longer sentences to be more syntactically complex as well as the increase of candidate governor number, some of it is due to longer sentences being more challenging to encode. This is especially relevant for low-resource scenarios such as parsing of written sources in historical languages (e.g. medieval and early-modern European languages), in particular legal texts, where sentences can be very long whereas the amount of training material remains limited. In this paper, we present a new method for explicitly using the arc length information in order to bias the scores produced by a graph-based parser. With a series of experiments on Norman and Gascon data, in which we divide the test data according to sentence length, we show that indeed explicit length coding is beneficial to retain parsing performance for longer sentences."
}