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

Published: 08 Jun 2022, Last Modified: 05 May 2023DLG4NLP 2022 OralReaders: Everyone
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
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