@inproceedings{vanroy-van-de-cruys-2024-less,
    title = "Less is Enough: Less-Resourced Multilingual {AMR} Parsing",
    author = "Vanroy, Bram  and
      Van de Cruys, Tim",
    editor = "Bunt, Harry  and
      Ide, Nancy  and
      Lee, Kiyong  and
      Petukhova, Volha  and
      Pustejovsky, James  and
      Romary, Laurent",
    booktitle = "Proceedings of the 20th Joint ACL - ISO Workshop on Interoperable Semantic Annotation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.isa-1.11",
    pages = "82--92",
    abstract = "This paper investigates the efficacy of multilingual models for the task of text-to-AMR parsing, focusing on English, Spanish, and Dutch. We train and evaluate models under various configurations, including monolingual and multilingual settings, both in full and reduced data scenarios. Our empirical results reveal that while monolingual models exhibit superior performance, multilingual models are competitive across all languages, offering a more resource-efficient alternative for training and deployment. Crucially, our findings demonstrate that AMR parsing benefits from transfer learning across languages even when having access to significantly smaller datasets. As a tangible contribution, we provide text-to-AMR parsing models for the aforementioned languages as well as multilingual variants, and make available the large corpora of translated data for Dutch, Spanish (and Irish) that we used for training them in order to foster AMR research in non-English languages. Additionally, we open-source the training code and offer an interactive interface for parsing AMR graphs from text.",
}

This paper investigates the efficacy of multilingual models for the task of text-to-AMR parsing, focusing on English, Spanish, and Dutch. We train and evaluate models under various configurations, including monolingual and multilingual settings, both in full and reduced data scenarios. Our empirical results reveal that while monolingual models exhibit superior performance, multilingual models are competitive across all languages, offering a more resource-efficient alternative for training and deployment. Crucially, our findings demonstrate that AMR parsing benefits from transfer learning across languages even when having access to significantly smaller datasets. As a tangible contribution, we provide text-to-AMR parsing models for the aforementioned languages as well as multilingual variants, and make available the large corpora of translated data for Dutch, Spanish (and Irish) that we used for training them in order to foster AMR research in non-English languages. Additionally, we open-source the training code and offer an interactive interface for parsing AMR graphs from text.
Vanroy, Bram Van de Cruys, Tim
Less is Enough: Less-Resourced Multilingual AMR Parsing