annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
tags:
- data-to-text-generation
dataset_info:
features:
- name: triple
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1343187306
num_examples: 6371131
- name: validation
num_bytes: 167790917
num_examples: 796471
- name: test
num_bytes: 167921750
num_examples: 796493
download_size: 1631259869
dataset_size: 1678899973
Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/google-research-datasets/KELM-corpus
- Repository: https://github.com/google-research-datasets/KELM-corpus
- Paper: https://arxiv.org/abs/2010.12688
- Leaderboard:
- Point of Contact:
Dataset Summary
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text. The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
Supported Tasks and Leaderboards
The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model with the tuples concatenated into a single sequence.
Languages
The dataset is in English.
Dataset Structure
Data Instances
Each instance consists of one KG triple paired with corresponding natural language.
Data Fields
triple: Wikipedia triples of the form<subject> <relation> <object>where some subjects have multiple relations, e.g.<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>. For more details on how these relations are grouped, please refer to the paper.sentence: The corresponding Wikipedia sentence.
Data Splits
The dataset includes a pre-determined train, validation, and test split.
Dataset Creation
Curation Rationale
The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate natural text from a knowledge graph.
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
The data is sourced from English Wikipedia and it's associated knowledge graph.
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
From the paper:
Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still contain some of these biases, certain types of biases may be reduced.
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
This dataset has been released under the CC BY-SA 2.0 license.
Citation Information
@misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contributions
Thanks to @joeddav for adding this dataset.