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

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

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

ToTTo: A Controlled Table-To-Text Generation Dataset

EMNLP 2020 google-research-datasets/ToTTo

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 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.

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

Few-shot Natural Language Generation for Task-Oriented Dialog

27 Feb 2020pengbaolin/SC-GPT

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

Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation

WS 2019 AmitMY/chimera

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.

4 DATA-TO-TEXT GENERATION