KG-to-Text Generation
16 papers with code • 11 benchmarks • 9 datasets
Knowledge-graph-to-text (KG-to-text) generation aims to generate high-quality texts which are consistent with input graphs.
Description from: JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
Benchmarks
These leaderboards are used to track progress in KG-to-Text Generation
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WebQuestions
WebNLG
AGENDA
WikiGraphs
EventNarrative
XAlign
Most implemented papers
Text Generation from Knowledge Graphs with Graph Transformers
rikdz/GraphWriter
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NAACL 2019
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Investigating Pretrained Language Models for Graph-to-Text Generation
UKPLab/plms-graph2text
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EMNLP (NLP4ConvAI) 2021
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Deep Graph Convolutional Encoders for Structured Data to Text Generation
diegma/graph-2-text
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WS 2018
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
Handling Rare Items in Data-to-Text Generation
shimorina/webnlg-dataset
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WS 2018
Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism.
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.
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
hugochan/Graph2Seq-for-KGQG
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In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
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.
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text.
How to Train Your Agent to Read and Write
menggehe/DRAW
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Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice.
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
RUCAIBox/Few-Shot-KG2Text
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Findings (ACL) 2021
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG).
