Data-to-Text Generation
60 papers with code • 19 benchmarks • 18 datasets
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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Datasets
MultiWOZ
WebNLG
E2E
RotoWire
KVRET
RoboCup
ToTTo
ViGGO
Greatest papers with code
Language Models are Unsupervised Multitask Learners
huggingface/transformers
•
•
Preprint 2019
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)
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
budzianowski/multiwoz
•
•
ACL 2019
Semantically controlled neural response generation on limited-domain has achieved great performance.
Ranked #5 on
Data-to-Text Generation
on MULTIWOZ 2.1
Text-to-Text Pre-Training for Data-to-Text Tasks
We study the pre-train + fine-tune strategy for data-to-text tasks.
Ranked #1 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
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
Findings of the E2E NLG Challenge
UFAL-DSG/tgen
•
•
WS 2018
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
UFAL-DSG/tgen
•
•
WS 2017
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.
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
huseinzol05/malay-dataset
•
•
NAACL 2021
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets.
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
diegma/graph-2-text
•
•
WS 2018
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods.
Ranked #2 on
Data-to-Text Generation
on SR11Deep