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.

Most implemented papers

An Incremental Parser for Abstract Meaning Representation

mdtux89/amr-evaluation EACL 2017

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 28 May 2017

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


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

freesunshine0316/sembleu ACL 2019

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

muyeby/amrbart ACL 2022

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.