Data-to-Text Generation Edit Task
Natural Language Processing • Text Generation
About
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 )
Benchmarks
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Subtasks
Datasets
Greatest papers with code
Language Models are Unsupervised Multitask Learners
Preprint 2019
• huggingface/transformers
• 
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
Ranked #1 on
Language Modelling
on enwik8
(using extra training data)
4 COMMON SENSE REASONING DATA-TO-TEXT GENERATION DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING QUESTION ANSWERING READING COMPREHENSION
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
ACL 2019
• budzianowski/multiwoz
•
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on
Data-to-Text Generation
on MULTIWOZ 2.1
ToTTo: A Controlled Table-To-Text Generation Dataset
We present ToTTo, an open-domain English table-to-text dataset with over 120, 000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
Ranked #2 on
Data-to-Text Generation
on ToTTo
CONDITIONAL TEXT GENERATION DATA-TO-TEXT GENERATION TABLE-TO-TEXT GENERATION
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.
Ranked #4 on
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
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
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.
Ranked #1 on
Data-to-Text Generation
on SR11Deep
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.
Few-shot Natural Language Generation for Task-Oriented Dialog
It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.
Ranked #4 on
Data-to-Text Generation
on MULTIWOZ 2.1
Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation
We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.