A Graph-to-Sequence Model for AMR-to-Text Generation

ACL 2018  ·  Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea ·

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.

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 Ranked #1 on Graph-to-Sequence on LDC2015E86: (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Graph-to-Sequence LDC2015E86: GRN BLEU 33.6 # 1
Text Generation LDC2016E25 Graph2Seq BLEU 22 # 1