Generating syntactically varied realisations from AMR graphs
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Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. We learn a sequence-to-sequence model that generates possible constituency trees for an AMR graph, and then train another model to generate text realisations conditioned on both an AMR graph and a constituency tree. We show that factorising the model this way lets us effectively use parse information, obtaining competitive BLEU scores on self-generated parses and impressive BLEU scores with oracle parses. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph.
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