Papers with Code 아카이브

Graph-to-Sequence

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

2

보존된 평가 기록

3

코드

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

https://github.com/AlanSwift/DH-VQG

공식 코드

Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference

https://github.com/AmitMY/chimera

공식 코드

Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

https://github.com/Cartus/DCGCN

공식 코드

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

https://github.com/IBM/GPT-too-AMR2text

공식 코드

GPT-too: A language-model-first approach for AMR-to-text generation

https://github.com/IBM/Graph2Seq

공식 코드

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

https://github.com/IBM/SQL-to-Text

공식 코드

SQL-to-Text Generation with Graph-to-Sequence Model

https://github.com/IBM/Text-to-LogicForm

공식 코드

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

https://github.com/LiyingCheng95/EntityDescriptionGeneration

공식 코드

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

https://github.com/PML-UCF/pinn

공식 코드

Fleet Prognosis with Physics-informed Recurrent Neural Networks

https://github.com/UKPLab/emnlp2019-dualgraph

공식 코드

Enhancing AMR-to-Text Generation with Dual Graph Representations

https://github.com/UKPLab/kg2text

공식 코드

Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

https://github.com/beckdaniel/acl2018_graph2seq

공식 코드

Graph-to-Sequence Learning using Gated Graph Neural Networks

https://github.com/coleygroup/graph2smiles

공식 코드

Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction

https://github.com/daehuikim/WikiOFGraph

공식 코드

Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model

https://github.com/diegma/graph-2-text

공식 코드

Deep Graph Convolutional Encoders for Structured Data to Text Generation

https://github.com/freesunshine0316/neural-graph-to-seq-mp

공식 코드

A Graph-to-Sequence Model for AMR-to-Text Generation

https://github.com/hugochan/RL-based-Graph2Seq-for-NQG

공식 코드

Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation

https://github.com/jcyk/gtos

공식 코드

Graph Transformer for Graph-to-Sequence Learning

https://github.com/lancopku/Graph-to-seq-comment-generation

공식 코드

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

https://github.com/mdtux89/OpenNMT-py

공식 코드

Structural Neural Encoders for AMR-to-text Generation

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

공식 코드

Structural Neural Encoders for AMR-to-text Generation

https://github.com/yuya-s/satanderparking

공식 코드

Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

https://github.com/zengkaipeng/UAlign

공식 코드

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment

https://github.com/Attn-to-FC/Attn-to-FC

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

https://github.com/LeiBAI/STG2Seq

STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

https://github.com/ReleasedBrainiac/GraphToSequenceNN

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

https://github.com/dice-group/NABU

Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

https://github.com/facebookresearch/m-amr2text

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

https://github.com/sinantie/NeuralAmr

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

https://github.com/yangyucheng000/Papers/tree/main/amr2text

Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

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