It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (short for abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.
GRNs have recently been used to model graph structures in NLP tasks. In particular, use a GRN model to represent raw sentences by building a graph structure of neighboring words and a sentence-level node, showing that the encoder outperforms BiLSTMs and Transformer @cite_29 on classification and sequence labeling tasks; build a GRN for encoding AMR graphs for text generation, showing that the representation is superior compared to BiLSTM on serialized AMR. We extend by investigating the usefulness of AMR for neural machine translation. To our knowledge, we are the first to use GRN for machine translation.
@cite_29
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The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.