@inproceedings{sobrevilla-cabezudo-pardo-2022-exploring,
    title = "Exploring a {POS}-based Two-stage Approach for Improving Low-Resource {AMR}-to-Text Generation",
    author = "Sobrevilla Cabezudo, Marco Antonio  and
      Pardo, Thiago",
    editor = "Bosselut, Antoine  and
      Chandu, Khyathi  and
      Dhole, Kaustubh  and
      Gangal, Varun  and
      Gehrmann, Sebastian  and
      Jernite, Yacine  and
      Novikova, Jekaterina  and
      Perez-Beltrachini, Laura",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.gem-1.49/",
    doi = "10.18653/v1/2022.gem-1.49",
    pages = "531--538",
    abstract = "This work presents a two-stage approach for tackling low-resource AMR-to-text generation for Brazilian Portuguese. Our approach consists of (1) generating a masked surface realization in which some tokens are masked according to its Part-of-Speech class and (2) infilling the masked tokens according to the AMR graph and the previous masked surface realization. Results show a slight improvement over the baseline, mainly in BLEU (1.63) and METEOR (0.02) scores. Moreover, we evaluate the pipeline components separately, showing that the bottleneck of the pipeline is the masked surface realization. Finally, the human evaluation suggests that models still suffer from hallucinations, and some strategies to deal with the problems found are proposed."
}