@inproceedings{li-flanigan-2022-improving,
    title = "Improving Neural Machine Translation with the {A}bstract {M}eaning {R}epresentation by Combining Graph and Sequence Transformers",
    author = "Li, Changmao  and
      Flanigan, Jeffrey",
    editor = "Wu, Lingfei  and
      Liu, Bang  and
      Mihalcea, Rada  and
      Pei, Jian  and
      Zhang, Yue  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.dlg4nlp-1.2",
    doi = "10.18653/v1/2022.dlg4nlp-1.2",
    pages = "12--21",
    abstract = "Previous studies have shown that the Abstract Meaning Representation (AMR) can improve Neural Machine Translation (NMT). However, there has been little work investigating incorporating AMR graphs into Transformer models. In this work, we propose a novel encoder-decoder architecture which augments the Transformer model with a Heterogeneous Graph Transformer (Yao et al., 2020) which encodes source sentence AMR graphs. Experimental results demonstrate the proposed model outperforms the Transformer model and previous non-Transformer based models on two different language pairs in both the high resource setting and low resource setting. Our source code, training corpus and released models are available at \url{https://github.com/jlab-nlp/amr-nmt}.",
}