@inproceedings{kang-etal-2025-reassessing,
    title = "Reassessing Graph Linearization for Sequence-to-sequence {AMR} Parsing: On the Advantages and Limitations of Triple-Based",
    author = "Kang, Jeongwoo  and
      Coavoux, Maximin  and
      Schwab, Didier  and
      Lopez, C{\'e}dric",
    editor = "Drozd, Aleksandr  and
      Sedoc, Jo{\~a}o  and
      Tafreshi, Shabnam  and
      Akula, Arjun  and
      Shu, Raphael",
    booktitle = "The Sixth Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.insights-1.3/",
    pages = "15--23",
    ISBN = "979-8-89176-240-4",
    abstract = "Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al.,2013, AMR) parsers. To train such models, AMR graphs have to be linearized into a one-line text format. While Penman encoding is widely used for this purpose, we argue that it has limitations: 1) for deep graphs, some closely related nodes are located far apart in the linearized text 2) Penman{'}s tree-based encoding necessitates inverse roles to handle node re-entrancy, doubling the number of relation types to predict. To address these issues, we propose a triple-based linearization method and compare its efficiency by training an AMR parser with both approaches. Although triple is well suited to represent a graph, our results show that it does not yet improve performance on deeper or longer graphs. It suggests room for improvement in its design to better compete with Penman{'}s concise representation and explicit encoding of a nested graph structure."
}