Graph-to-Sequence
26 papers with code • 2 benchmarks • 3 datasets
Mapping an input graph to a sequence of vectors.
Libraries
Use these libraries to find Graph-to-Sequence models and implementations
WebNLG
EventNarrative
ENT-DESC
Latest papers
UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment
Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science.
Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander
Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world.
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged.
GPT-too: A language-model-first approach for AMR-to-text generation
IBM/GPT-too-AMR2text
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ACL 2020
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
A physics-informed neural network for wind turbine main bearing fatigue
PML-UCF/pinn
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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).
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
LiyingCheng95/EntityDescriptionGeneration
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EMNLP 2020
Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
UKPLab/kg2text
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Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations.
Graph Transformer for Graph-to-Sequence Learning
jcyk/gtos
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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.
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
hugochan/RL-based-Graph2Seq-for-NQG
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Natural question generation (QG) aims to generate questions from a passage and an answer.
Physics-informed neural networks for corrosion-fatigue prognosis
PML-UCF/pinn
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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).
