Datasets:
Tasks:
Text2Text Generation
Sub-tasks:
text-simplification
Languages:
German
Size:
10K<n<100K
ArXiv:
License:
Create README.md
Browse files
README.md
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---
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task_categories:
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- text2text-generation
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task_ids:
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- text-simplification
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language:
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- de
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size_categories:
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- 10K<n<100K
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pretty_name: DEplain-APA
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configs:
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- simplification
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---
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# Dataset Card for DEplain-APA
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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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## Dataset Description
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- **Repository:** [DEplain-APA zenodo repository](https://zenodo.com/)
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- **Paper:** [DEplain: ]()
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- **Point of Contact:** [Regina Stodden](regina.stodden@hhu.de)
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### Dataset Summary
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[DEplain-APA](https://zenodo.com) [(Stodden et al., 2022)]() is a dataset for the training and evaluation of sentence and document simplification in German. All texts of this dataset are provided by the Austrian Press Agency. The simple-complex sentence pairs are manually aligned.
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### Supported Tasks and Leaderboards
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The dataset supports the training and evaluation of `text-simplification` systems. Success in this task is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf).
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### Languages
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The text in this dataset is in Austrian German (`de-at`).
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### Domains
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All texts in this dataset are news data.
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## Dataset Structure
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### Data Access
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- The dataset is licensed with restricted access for only academic purposes. To download the dataset, please request access on [zenodo]().
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### Data Instances
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- `document-simplification` configuration: an instance consists of an original document and one reference simplification.
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- `sentence-simplification` configuration: an instance consists of an original sentence and one manually aligned reference simplification.
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### Data Fields
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- `original`: an original text from the source datasets written for people with German skills equal to CEFR level B1
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- `simplification`: a simplified text from the source datasets written for people with German skills equal to CEFR level A2
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- more metadata is added to the dataset
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### Data Splits
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DEplain-APA is randomly split into a training, development and test set. The training set of the sentence-simplification configuration contains only texts of documents which are part of the training set of document-simplification configuration and the same for dev and test sets.
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The statistics are given below.
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| | Train | Dev | Test | Total |
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| ----- | ------ | ------ | ---- | ----- |
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| Document Pairs | | | |
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| Sentence Pairs | | | |
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The test and validation sets are the same as those of TurkCorpus. The split was random.
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Here, more information on simplification operations will follow soon.
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## Dataset Creation
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### Curation Rationale
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DEplain-APA was created to improve the training and evaluation of German document and sentence simplification. The data is provided by the same data provided as for the APA-LHA data. In comparison to APA-LHA (automatic-aligned), the sentence pairs of DEplain-APA are all manually aligned. Further, DEplain-APA aligns the texts in language level B1 with the texts in A2, which result in mostly mild simplifications.
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Further DEplain-APA, contains parallel documents as well as parallel sentence pairs.
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### Source Data
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#### Initial Data Collection and Normalization
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The original news texts (in CEFR level C2) were manually simplified by professional translators, i.e. capito – CFS GmbH, and provided to us by the Austrian Press Agency.
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All documents date back to 2019 to 2021 (?).
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Two German native speakers have manually aligned the sentence pairs by using the text simplification annotation tool TS-ANNO. The data was split into sentences using a German model of SpaCy.
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#### Who are the source language producers?
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The original news texts (in CEFR level C2) were manually simplified by professional translators, i.e. capito – CFS GmbH. No other demographic or compensation information is known.
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### Annotations
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#### Annotation process
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The instructions given to the annotators are available [here](https://github.com/rstodden/TS_annotation_tool/tree/master/annotation_schema).
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#### Who are the annotators?
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The annotators are two German native speakers, who are trained in linguistics. Both were at least compensated with the minimum wage of their country of residence.
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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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[More Information Needed]
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### Other Known Limitations
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The dataset is provided for research purposes only. Please check the dataset license for additional information.
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## Additional Information
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### Dataset Curators
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ASSET was developed by researchers at the Heinrich-Heine-University Düsseldorf, Germany. This research is part of the PhD-program ``Online Participation'', supported by the North Rhine-Westphalian (German) funding scheme ``Forschungskolleg''.
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### Licensing Information
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[More Information Needed]
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### Citation Information
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[More Information Needed]
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This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r) and [Yacine Jernite](https://github.com/yjernite).
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