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Data-to-Text Generation

18 papers with code · Natural Language Processing
Subtask of Text Generation

Data-to-text generation is the task of generating text from a data source.

( Image credit: Data-to-Text Generation with Content Selection and Planning )

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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.

DATA-TO-TEXT GENERATION SPOKEN DIALOGUE SYSTEMS

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.

DATA-TO-TEXT GENERATION

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.

DATA-TO-TEXT 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.

DATA-TO-TEXT GENERATION GRAPH-TO-SEQUENCE

Data-to-Text Generation with Content Selection and Planning

3 Sep 2018ratishsp/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.

DATA-TO-TEXT GENERATION

Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

NAACL 2019 AmitMY/chimera

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.

DATA-TO-TEXT GENERATION GRAPH-TO-SEQUENCE

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.

DATA-TO-TEXT GENERATION REPRESENTATION LEARNING

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

2 Nov 2017felix-last/kmeans_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.

DATA-TO-TEXT GENERATION