AMR Parsing
49 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
Latest papers
AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite
We present the Granular AMR Parsing Evaluation Suite (GrAPES), a challenge set for Abstract Meaning Representation (AMR) parsing with accompanying evaluation metrics.
AMR Parsing with Causal Hierarchical Attention and Pointers
Translation-based AMR parsers have recently gained popularity due to their simplicity and effectiveness.
Guiding AMR Parsing with Reverse Graph Linearization
pkunlp-icler/amr_reverse_graph_linearization
•
•
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence.
Incorporating Graph Information in Transformer-based AMR Parsing
Abstract Meaning Representation (AMR) is a Semantic Parsing formalism that aims at providing a semantic graph abstraction representing a given text.
AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing
In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions.
Cross-domain Generalization for AMR Parsing
goodbai-nlp/amr-domainadaptation
•
•
Based on our observation, we investigate two approaches to reduce the domain distribution divergence of text and AMR features, respectively.
Better Smatch = Better Parser? AMR evaluation is not so simple anymore
Recently, astonishing advances have been observed in AMR parsing, as measured by the structural Smatch metric.
A Two-Stage Method for Chinese AMR Parsing
pkunlp-icler/two-stage-camrp
•
•
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation.
Inducing and Using Alignments for Transition-based AMR Parsing
IBM/transition-amr-parser
•
•
NAACL 2022
These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.
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.
