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 )

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)

Common Sense Reasoning Data-to-Text Generation +6

ToTTo: A Controlled Table-To-Text Generation Dataset

google-research-datasets/ToTTo EMNLP 2020

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.

Conditional Text Generation Data-to-Text Generation +1

Few-shot Natural Language Generation for Task-Oriented Dialog

thu-coai/ConvLab-2 Findings of the Association for Computational Linguistics 2020

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.

Data-to-Text Generation Few-Shot Learning

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.

Data-to-Text Generation Spoken Dialogue Systems

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.

Data-to-Text Generation

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.

Data-to-Text Generation Language Modelling

Challenges in Data-to-Document Generation

harvardnlp/data2text EMNLP 2017

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

diegma/graph-2-text WS 2018

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