@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}.",
}
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<title>Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)</title>
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<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 https://github.com/jlab-nlp/amr-nmt.</abstract>
<identifier type="citekey">li-flanigan-2022-improving</identifier>
<identifier type="doi">10.18653/v1/2022.dlg4nlp-1.2</identifier>
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<url>https://aclanthology.org/2022.dlg4nlp-1.2</url>
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<date>2022-07</date>
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%0 Conference Proceedings
%T Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers
%A Li, Changmao
%A Flanigan, Jeffrey
%Y Wu, Lingfei
%Y Liu, Bang
%Y Mihalcea, Rada
%Y Pei, Jian
%Y Zhang, Yue
%Y Li, Yunyao
%S Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F li-flanigan-2022-improving
%X 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 https://github.com/jlab-nlp/amr-nmt.
%R 10.18653/v1/2022.dlg4nlp-1.2
%U https://aclanthology.org/2022.dlg4nlp-1.2
%U https://doi.org/10.18653/v1/2022.dlg4nlp-1.2
%P 12-21
Markdown (Informal)
[Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers](https://aclanthology.org/2022.dlg4nlp-1.2) (Li & Flanigan, DLG4NLP 2022)
ACL