25 papers with code • 2 benchmarks • 2 datasets

Mapping an input graph to a sequence of vectors.


Use these libraries to find Graph-to-Sequence models and implementations
3 papers

Most implemented papers

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

freesunshine0316/neural-graph-to-seq-mp ACL 2017

Sequence-to-sequence models have shown strong performance across a broad range of applications.

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

IBM/Graph2Seq ICLR 2019

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.

Graph-to-Sequence Learning using Gated Graph Neural Networks

beckdaniel/acl2018_graph2seq ACL 2018

Many NLP applications can be framed as a graph-to-sequence learning problem.

Deep Graph Convolutional Encoders for Structured Data to Text Generation

diegma/graph-2-text WS 2018

Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.

Structural Neural Encoders for AMR-to-text Generation

mdtux89/OpenNMT-py-AMR-to-text NAACL 2019

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.

Physics-informed neural networks for corrosion-fatigue prognosis

PML-UCF/pinn Annual Conference of the PHM Society 2019

The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion).

A physics-informed neural network for wind turbine main bearing fatigue

PML-UCF/pinn International Journal of Prognostics and Health Management 2020

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss).

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

freesunshine0316/neural-graph-to-seq-mp ACL 2018

The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.

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

IBM/Text-to-LogicForm EMNLP 2018

Existing neural semantic parsers mainly utilize a sequence encoder, i. e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees.

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

IBM/SQL-to-Text EMNLP 2018

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.