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Found 10 papers, 7 papers with code
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing
We evaluate our oracle and parser using the Abstract Meaning Representation (AMR) parsing 3. 0 corpus.
Inducing and Using Alignments for Transition-based AMR Parsing
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.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
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.
Ranked #1 on
AMR Parsing
on LDC2020T02
(using extra training data)
Ensembling Graph Predictions for AMR Parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
Ranked #2 on
AMR Parsing
on LDC2020T02
(using extra training data)
EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition
Self-supervised ASR-TTS models suffer in out-of-domain data conditions.
Bootstrapping Multilingual AMR with Contextual Word Alignments
We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.
Pushing the Limits of AMR Parsing with Self-Learning
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.
Ranked #2 on
AMR Parsing
on LDC2014T12
Transition-based Parsing with Stack-Transformers
Modeling the parser state is key to good performance in transition-based parsing.
Ranked #19 on
AMR Parsing
on LDC2017T10
GPT-too: A language-model-first approach for AMR-to-text generation
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #10 on
AMR-to-Text Generation
on LDC2017T10
