@inproceedings{lindemann-etal-2019-compositional,
    title = "Compositional Semantic Parsing across Graphbanks",
    author = "Lindemann, Matthias  and
      Groschwitz, Jonas  and
      Koller, Alexander",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P19-1450/",
    doi = "10.18653/v1/P19-1450",
    pages = "4576--4585",
    abstract = "Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS."
}