A Graph-to-Sequence Model for AMR-to-Text Generation
TL
TL;DR
This work introduces a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics, and shows superior results to existing methods in the literature.
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 facing 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.
References34 items
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Leveraging Context Information for Natural Question Generation
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Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
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Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
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RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
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