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Joint Multilingual Sentence Representations
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations
Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope
Exploiting Reasoning Chains for Multi-hop Science Question Answering
A Primer on Pretrained Multilingual Language Models
Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignments
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing
Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
Semantic Representation for Dialogue Modeling
ASQ: Automatically Generating Question-Answer Pairs using AMRs
Polynomial Graph Parsing with Non-Structural Reentrancies
AMR Parsing with Action-Pointer Transformer
Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning
Transition-based Parsing with Stack-Transformers
Pushing the Limits of AMR Parsing with Self-Learning
Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation
Improving AMR Parsing with Sequence-to-Sequence Pre-training
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR
From Spatial Relations to Spatial Configurations
A Human Evaluation of AMR-to-English Generation Systems
AMR Similarity Metrics from Principles
Graph Transformer for Graph-to-Sequence Learning
Core Semantic First: A Top-down Approach for AMR Parsing
Enhancing AMR-to-Text Generation with Dual Graph Representations
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Separating Argument Structure from Logical Structure in AMR
SEMA: an Extended Semantic Evaluation Metric for AMR
Neural Text Generation from Rich Semantic Representations
Automatic Accuracy Prediction for AMR Parsing
An Improved Approach for Semantic Graph Composition with CCG
Structural Neural Encoders for AMR-to-text Generation
EAT: a simple and versatile semantic representation format for multi-purpose NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.