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AMR Parsing

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보존된 평가 기록: 75

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ChunchuanLv/AMR_AS_GRAPH_PREDICTION

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AMR Parsing as Graph Prediction with Latent Alignment

https://github.com/Emvista/Meta-XAMR-2024

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Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing

https://github.com/Emvista/Triple_AMR_Parser

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Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding

https://github.com/Heidelberg-NLP/simple-xamr

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Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing

https://github.com/IBM/transition-amr-parser

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Transition-based Parsing with Stack-Transformers

https://github.com/Oneplus/tamr

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An AMR Aligner Tuned by Transition-based Parser

https://github.com/RikVN/AMR

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Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations

https://github.com/ablodge/leamr

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Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments

https://github.com/aghie/tb-amr

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A Transition-based Algorithm for Unrestricted AMR Parsing

https://github.com/babelscape/amrs-assemble

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AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing

https://github.com/chenllliang/atp

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ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

https://github.com/didzis/smatchTools

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RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

https://github.com/didzis/tensorflowAMR

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RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

https://github.com/emorynlp/elit

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ELIT: Emory Language and Information Toolkit

https://github.com/emorynlp/levi-graph-amr-parser

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Levi Graph AMR Parser using Heterogeneous Attention

https://github.com/freesunshine0316/sembleu

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SemBleu: A Robust Metric for AMR Parsing Evaluation

https://github.com/goodbai-nlp/amr-domainadaptation

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Cross-domain Generalization for AMR Parsing

https://github.com/headacheboy/cross-lingual-amr-parsing

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Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing

https://github.com/heidelberg-nlp/amrparseeval

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Better Smatch = Better Parser? AMR evaluation is not so simple anymore

https://github.com/ibm/amr-annotations

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Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

https://github.com/ibm/graph_ensemble_learning

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Ensembling Graph Predictions for AMR Parsing

https://github.com/janmbuys/DeepDeepParser

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Robust Incremental Neural Semantic Graph Parsing

https://github.com/jcyk/AMR-gs

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AMR Parsing via Graph-Sequence Iterative Inference

https://github.com/jcyk/AMR-parser

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Core Semantic First: A Top-down Approach for AMR Parsing

https://github.com/jcyk/xamr

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Multilingual AMR Parsing with Noisy Knowledge Distillation

https://github.com/jgroschwitz/grapes

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AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite

https://github.com/louchao98/chap_amr_parser

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AMR Parsing with Causal Hierarchical Attention and Pointers

https://github.com/mdtux89/amr-evaluation

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An Incremental Parser for Abstract Meaning Representation

https://github.com/muyeby/amrbart

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Graph Pre-training for AMR Parsing and Generation

https://github.com/pkunlp-icler/amr_reverse_graph_linearization

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Guiding AMR Parsing with Reverse Graph Linearization

https://github.com/pkunlp-icler/atp

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ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

https://github.com/pkunlp-icler/two-stage-camrp

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A Two-Stage Method for Chinese AMR Parsing

https://github.com/sapienzanlp/leakdistill

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Incorporating Graph Information in Transformer-based AMR Parsing

https://github.com/sheng-z/stog

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AMR Parsing as Sequence-to-Graph Transduction

https://github.com/wangpeiyi9979/hcl-text2amr

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Hierarchical Curriculum Learning for AMR Parsing

https://github.com/xdqkid/S2S-AMR-Parser

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Improving AMR Parsing with Sequence-to-Sequence Pre-training

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