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arXiv · 1805.02473

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

Linfeng Song·Yue Zhang·Zhiguo Wang·Daniel Gildea
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Abstract

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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Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea. 2018-05-07. A Graph-to-Sequence Model for AMR-to-Text Generation. https://arxiv.org/abs/1805.02473

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