Abstract Meaning Representation
86 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Abstract Meaning Representation
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.
AMR Similarity Metrics from Principles
Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs.
Neuro-symbolic Commonsense Social Reasoning
We present a novel system for taking social rules of thumb (ROTs) in natural language from the Social Chemistry 101 dataset and converting them to first-order logic where reasoning is performed using a neuro-symbolic theorem prover.
Text Summarization using Abstract Meaning Representation
With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding.
Structural Neural Encoders for AMR-to-text Generation
mdtux89/OpenNMT-py-AMR-to-text
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NAACL 2019
AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs.
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
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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.
Persian Abstract Meaning Representation
Abstract Meaning Representation (AMR) is an annotation framework representing the semantic structure of a sentence as a whole.
