AMR Parsing Edit Task
Natural Language Processing • Semantic Parsing
About
Each AMR is a single rooted, directed graph. AMRs include PropBank semantic roles, within-sentence coreference, named entities and types, modality, negation, questions, quantities, and so on. See.
Benchmarks
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Datasets
Greatest papers with code
AMR Parsing as Sequence-to-Graph Transduction
ACL 2019
• sheng-z/stog
• 
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).
Ranked #1 on
AMR Parsing
on LDC2014T12:
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
ACL 2017
• freesunshine0316/neural-graph-to-seq-mp
• 
Sequence-to-sequence models have shown strong performance across a broad range of applications.
Ranked #6 on
AMR Parsing
on LDC2015E86
AMR Parsing via Graph-Sequence Iterative Inference
ACL 2020
• jcyk/AMR-gs
•
We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph.
Ranked #2 on
AMR Parsing
on LDC2014T12
(F1 Full metric)
AMR Parsing as Graph Prediction with Latent Alignment
ACL 2018
• ChunchuanLv/AMR_AS_GRAPH_PREDICTION
•
AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences.
Ranked #1 on
AMR Parsing
on LDC2015E86
An Incremental Parser for Abstract Meaning Representation
EACL 2017
• mdtux89/amr-eager
• 
We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.
Ranked #5 on
AMR Parsing
on LDC2015E86
AMR PARSING NAMED ENTITY RECOGNITION SEMANTIC ROLE LABELING WORD SENSE DISAMBIGUATION
Robust Incremental Neural Semantic Graph Parsing
Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing.
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
• RikVN/AMR
•
We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).
Ranked #9 on
AMR Parsing
on LDC2017T10
Improving AMR Parsing with Sequence-to-Sequence Pre-training
EMNLP 2020
• xdqkid/S2S-AMR-Parser
•
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Ranked #1 on
AMR Parsing
on LDC2017T10