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SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.18653/v1/d15-1136openalex-snapshot
Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation
inferredcites →https://doi.org/10.18653/v1/e17-1051openalex-snapshot
An Incremental Parser for Abstract Meaning Representation
inferredcites →https://doi.org/10.18653/v1/n16-1087openalex-snapshot
Generation from Abstract Meaning Representation using Tree Transducers
inferredcites →https://doi.org/10.18653/v1/s16-1166openalex-snapshot
SemEval-2016 Task 8: Meaning Representation Parsing
inferredcites →https://doi.org/10.18653/v1/s16-1176openalex-snapshot
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
inferredcites →https://doi.org/10.18653/v1/s16-1181openalex-snapshot
CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser
inferredcites →https://doi.org/10.18653/v1/s16-1185openalex-snapshot
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
inferredcites →https://doi.org/10.18653/v1/s16-1186openalex-snapshot
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
inferredcites →https://doi.org/10.18653/v1/s17-2096openalex-snapshot
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
inferredcites →https://doi.org/10.18653/v1/s17-2156openalex-snapshot
UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR
inferredcites →https://doi.org/10.18653/v1/s17-2157openalex-snapshot
Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention
inferredcites →https://doi.org/10.18653/v1/s17-2158openalex-snapshot
FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers
inferredcites →https://doi.org/10.18653/v1/s17-2159openalex-snapshot
RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
inferredcites →https://doi.org/10.18653/v1/s17-2160openalex-snapshot
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
inferredcites →https://doi.org/10.18653/v1/w16-2301openalex-snapshot
Findings of the 2016 Conference on Machine Translation
inferredcites →https://doi.org/10.18653/v1/w16-6603openalex-snapshot
Generating English from Abstract Meaning Representations
inferredcites →https://doi.org/10.3115/v1/d14-1048openalex-snapshot
Aligning English Strings with Abstract Meaning Representation Graphs
inferredcites →https://doi.org/10.3115/v1/n15-1040openalex-snapshot
A Transition-based Algorithm for AMR Parsing
inferredcites →https://doi.org/10.3115/v1/p14-1134openalex-snapshot
A Discriminative Graph-Based Parser for the Abstract Meaning Representation
inferredcites →https://doi.org/10.3115/v1/p15-2141openalex-snapshot
Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers
inferredcites →https://doi.org/10.3115/v1/w14-3301openalex-snapshot
Efficient Elicitation of Annotations for Human Evaluation of Machine Translation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Abstract meaning representation of Turkish
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Contextualized Formula Search Using Math Abstract Meaning Representation
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Biases in Large Language Models: Origins, Inventory, and Discussion
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Deep Learning Approaches to Text Production. By Shashi Narayan and Claire Gardent (University of Edinburgh; CNRS/LORIA, Nancy). Morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 43), 2020, xxvi+175 pp; paperback, ISBN 978-1-68173-758-4, $79.95;…
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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
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Abstract Meaning Representation for Paraphrase Detection
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AMR dependency parsing with a typed semantic algebra
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Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language
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RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
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Automatic Accuracy Prediction for AMR Parsing
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Traitement automatique des langues et génération automatique d'exercices de grammaire
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