AMR Parsing
44 papers with code • 8 benchmarks • 6 datasets
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
These leaderboards are used to track progress in AMR Parsing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Bio
Groningen Meaning Bank
Most implemented papers
An Incremental Parser for Abstract Meaning Representation
We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
freesunshine0316/neural-graph-to-seq-mp
•
•
ACL 2017
Sequence-to-sequence models have shown strong performance across a broad range of applications.
AMR Parsing via Graph-Sequence Iterative Inference
jcyk/AMR-gs
•
•
ACL 2020
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.
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
didzis/tensorflowAMR
•
•
SEMEVAL 2016
The first extension com-bines the smatch scoring script with the C6. 0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs.
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).
AMR Parsing as Graph Prediction with Latent Alignment
ChunchuanLv/AMR_AS_GRAPH_PREDICTION
•
•
ACL 2018
AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences.
SemBleu: A Robust Metric for AMR Parsing Evaluation
Evaluating AMR parsing accuracy involves comparing pairs of AMR graphs.
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
IBM/transition-amr-parser
•
•
NAACL 2022
AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.
Graph Pre-training for AMR Parsing and Generation
To our knowledge, we are the first to consider pre-training on semantic graphs.
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
chenllliang/atp
•
•
Findings (NAACL) 2022
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing.
