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