Data-to-Text Generation
Data-to-text generation is the task of generating text from a data source.
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Greatest papers with code
Findings of the E2E NLG Challenge
WS 2018
• UFAL-DSG/tgen
• 
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems.
#3 best model for
Data-to-Text Generation
on E2E NLG Challenge
The E2E Dataset: New Challenges For End-to-End Generation
WS 2017
• UFAL-DSG/tgen
•
This paper describes the E2E data, a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
Challenges in Data-to-Document Generation
EMNLP 2017 • harvardnlp/data2text
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
#2 best model for
Data-to-Text Generation
on RotoWire (Relation Generation)
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
Data-to-Text Generation with Content Selection and Planning
• ratishsp/data2text-plan-py
•
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order.
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
Data-to-text Generation with Entity Modeling
ACL 2019
• ratishsp/data2text-entity-py
•
Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end.
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions.
Describing a Knowledge Base
WS 2018
• EagleW/Describing_a_Knowledge_Base
•
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
Pragmatically Informative Text Generation
NAACL 2019 • sIncerass/prag_generation
We improve the informativeness of models for conditional text generation using techniques from computational pragmatics.