AMR Parsing as Sequence-to-Graph Transduction

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).

Accepted at ACL 2019

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Character-Aware Neural Language Models
• Pointer Sentinel Mixture Models
• Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
• AMR Dependency Parsing with a Typed Semantic Algebra
• Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Cited by in corpus (3)

• Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
• AMR Parsing with Action-Pointer Transformer
• Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing