@inproceedings{vasylenko-etal-2023-incorporating,
    title = "Incorporating Graph Information in Transformer-based {AMR} Parsing",
    author = "Vasylenko, Pavlo  and
      Huguet Cabot, Pere Llu{\'\i}s  and
      Mart{\'\i}nez Lorenzo, Abelardo Carlos  and
      Navigli, Roberto",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.125",
    doi = "10.18653/v1/2023.findings-acl.125",
    pages = "1995--2011",
    abstract = "Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at [\url{http://www.github.com/sapienzanlp/LeakDistill}](\url{http://www.github.com/sapienzanlp/LeakDistill}).",
}

Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text. Current approaches are based on autoregressive language models such as BART or T5, fine-tuned through Teacher Forcing to obtain a linearized version of the AMR graph from a sentence. In this paper, we present LeakDistill, a model and method that explores a modification to the Transformer architecture, using structural adapters to explicitly incorporate graph information into the learned representations and improve AMR parsing performance. Our experiments show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing through self-knowledge distillation, even without the use of additional data. We release the code at [\url{http://www.github.com/sapienzanlp/LeakDistill}](\url{http://www.github.com/sapienzanlp/LeakDistill}).
Vasylenko, Pavlo Huguet Cabot, Pere Llu{\'\i}s Mart{\'\i}nez Lorenzo, Abelardo Carlos Navigli, Roberto
Incorporating Graph Information in Transformer-based AMR Parsing