Graph-to-Sequence
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Latest papers with code
Graph Transformer for Graph-to-Sequence Learning
• jcyk/gtos
• 
The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons.
GRAPH REPRESENTATION LEARNING GRAPH-TO-SEQUENCE MACHINE TRANSLATION TEXT GENERATION
Enhancing AMR-to-Text Generation with Dual Graph Representations
IJCNLP 2019
• UKPLab/emnlp2019-dualgraph
•
Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization.
#2 best model for
Data-to-Text Generation
on WebNLG
Structural Neural Encoders for AMR-to-text Generation
NAACL 2019
• mdtux89/OpenNMT-py-AMR-to-text
•
AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs.
#2 best model for
Graph-to-Sequence
on LDC2015E86:
Deep Graph Convolutional Encoders for Structured Data to Text Generation
WS 2018
• diegma/graph-2-text
•
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
SOTA for Data-to-Text Generation on WebNLG
SQL-to-Text Generation with Graph-to-Sequence Model
EMNLP 2018
• IBM/SQL-to-Text
• 
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query.
Graph-to-Sequence Learning using Gated Graph Neural Networks
ACL 2018
• beckdaniel/acl2018_graph2seq
• 
Many NLP applications can be framed as a graph-to-sequence learning problem.
A Graph-to-Sequence Model for AMR-to-Text Generation
ACL 2018
• freesunshine0316/neural-graph-to-seq-mp
•
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.
SOTA for Graph-to-Sequence on LDC2015E86: (using extra training data)
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings.
SOTA for SQL-to-Text on WikiSQL
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
ACL 2017
• freesunshine0316/neural-graph-to-seq-mp
•
Sequence-to-sequence models have shown strong performance across a broad range of applications.