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Abstract Meaning Representation

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코드

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

https://github.com/mdtux89/OpenNMT-py-AMR-to-text

공식 코드

Structural Neural Encoders for AMR-to-text Generation

https://github.com/mdtux89/amr-eager-multilingual

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Cross-lingual Abstract Meaning Representation Parsing

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

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An Incremental Parser for Abstract Meaning Representation

https://github.com/muyeby/AMR-Dialogue

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Semantic Representation for Dialogue Modeling

https://github.com/muyeby/amrbart

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Graph Pre-training for AMR Parsing and Generation

https://github.com/namir0806/flag

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FLAG: Financial Long Document Classification via AMR-based GNN

https://github.com/nhhoang96/cof-cot

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CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks

https://github.com/osome-iu/Rematch-RARE

공식 코드

Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity

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/tsar

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A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

https://github.com/pluslabnlp/ampere

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AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model

https://github.com/pluslabnlp/deam

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DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations

https://github.com/pournaki/soteu-narratives

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A graph-based approach to extracting narrative signals from public discourse

https://github.com/rafaelanchieta/sema

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SEMA: an Extended Semantic Evaluation Metric for AMR

https://github.com/sapienzanlp/leakdistill

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Incorporating Graph Information in Transformer-based AMR Parsing

https://github.com/sheffieldnlp/AMR2Text-summ

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Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation

https://github.com/shirawein/amr-translationese

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Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation

https://github.com/shlurbee/dmrs-text-generation-naacl2019

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Neural Text Generation from Rich Semantic Representations

https://github.com/strong-ai-lab/logical-equivalence-driven-amr-data-augmentation-for-representation-learning

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Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning

https://github.com/timoschick/amr-gen

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Transition-Based Generation from Abstract Meaning Representations

https://github.com/uclanlp/paraamr

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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

https://github.com/uic-nlp-lab/calsum

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Abstract Meaning Representation for Hospital Discharge Summarization

https://github.com/wangcunxiang/graph-as-tokens

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Exploiting Abstract Meaning Representation for Open-Domain Question Answering

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

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Hierarchical Curriculum Learning for AMR Parsing

https://github.com/wwxu21/AMR-SG

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Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

https://github.com/wwxu21/cgr

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Exploiting Reasoning Chains for Multi-hop Science Question Answering

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

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

https://github.com/yanzhang92/LDGCNs

공식 코드

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.