Amr Parsing
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.
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
ACL 2017
• freesunshine0316/neural-graph-to-seq-mp
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Sequence-to-sequence models have shown strong performance across a broad range of applications.
AMR Parsing as Sequence-to-Graph Transduction
ACL 2019
• sheng-z/stog
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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).
SOTA for Amr Parsing on LDC2014T12:
AMR Parsing as Graph Prediction with Latent Alignment
ACL 2018
• ChunchuanLv/AMR_AS_GRAPH_PREDICTION
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AMR parsing is challenging partly due to the lack of annotated alignments between nodes in the graphs and words in the corresponding sentences.
An AMR Aligner Tuned by Transition-based Parser
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser.
#2 best model for
Amr Parsing
on LDC2014T12:
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
• RikVN/AMR
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We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
SEMEVAL 2016
• didzis/tensorflowAMR
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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.
Core Semantic First: A Top-down Approach for AMR Parsing
The output graph spans the nodes by the distance to the root, following the intuition of first grasping the main ideas then digging into more details.