Structural Neural Encoders for AMR-to-text Generation

NAACL 2019 Marco DamonteShay B. Cohen

AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Graph-to-Sequence LDC2015E86: GCNSEQ BLEU 23.95 # 2