text: 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.
  @cite_15: Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.
  @cite_16: 
  @cite_32: 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.
summary: Recently, investigate semantic role labeling (SRL) on neural machine translation (NMT). The predicate-argument structures are encoded via graph convolutional network (GCN) layers @cite_15 , which are laid on top of regular BiRNN or CNN layers. Our work is in line with exploring semantic information, but different in exploiting AMR rather than SRL for NMT @. In addition, we leverage a graph recurrent network (GRN) @cite_32 @cite_16 for modeling AMRs rather than GCN, which is formally consistent with the RNN sentence encoder. Since there is no one-to-one correspondence between AMR nodes and source words, we adopt a doubly-attentive LSTM decoder, which is another major difference from .