2019
ACL
ACL 2019
AMR Parsing as Sequence-to-Graph Transduction
Abstract
AbstractWe 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% on LDC2017T10) and AMR 1.0 (70.2% on LDC2014T12).
🌉
Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— amr parsing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Transformers
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Semantic Parsing
Deep Learning > Learning Types > Sequence Modeling