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

ACL 2018 Linfeng SongYue ZhangZhiguo WangDaniel 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... (read more)

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Evaluation results from the paper

 SOTA for 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
Graph-to-Sequence LDC2015E86: GRN BLEU 33.6 # 1
Text Generation LDC2016E25 Graph2Seq BLEU 22 # 1