Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers

Changmao Li, Jeffrey Flanigan


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.
Anthology ID:
2022.dlg4nlp-1.2
Volume:
Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
DLG4NLP | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–21
Language:
URL:
https://aclanthology.org/2022.dlg4nlp-1.2
DOI:
10.18653/v1/2022.dlg4nlp-1.2
Bibkey:
Cite (ACL):
Changmao Li and Jeffrey Flanigan. 2022. Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers. In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022), pages 12–21, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers (Li & Flanigan, DLG4NLP 2022)
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PDF:
https://aclanthology.org/2022.dlg4nlp-1.2.pdf
Code
 jlab-nlp/amr-nmt