AMR Parsing as Sequence-to-Graph Transduction

Meeting of the Association for Computational Linguistics, 2019.

Cited by: 20|Views53
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Abstract:

We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental res...More

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