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Papers/AMR Parsing as Sequence-to-Graph Transduction

AMR Parsing as Sequence-to-Graph Transduction

Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

2019-05-21ACL 2019 7Semantic ParsingAMR Parsing
PaperPDFCode(official)

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 results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0 (70.2% F1 on LDC2014T12).

Results

TaskDatasetMetricValueModel
Semantic ParsingLDC2014T12F1 Full70.2Two-stage Sequence-to-Graph Transducer
Semantic ParsingLDC2017T10Smatch76.3Sequence-to-Graph Transduction
Semantic ParsingLDC2014T12:F1 Full0.7Sequence-to-Graph Transduction
Semantic ParsingLDC2014T12:F1 Newswire0.75Sequence-to-Graph Transduction
AMR ParsingLDC2014T12F1 Full70.2Two-stage Sequence-to-Graph Transducer
AMR ParsingLDC2017T10Smatch76.3Sequence-to-Graph Transduction
AMR ParsingLDC2014T12:F1 Full0.7Sequence-to-Graph Transduction
AMR ParsingLDC2014T12:F1 Newswire0.75Sequence-to-Graph Transduction

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