Datasets:
license:
- mit
language:
- de
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
task_categories:
- sentence-similarity
This dataset card is still a draft version. The dataset has not been uploaded yet.
This is a record of German language paraphrases. These are text pairs that have the same meaning but are expressed in different words. The source of the paraphrases are different parallel German / English text corpora. The English texts were machine translated back into German. This is how the paraphrases were obtained.
Parallel text corpora used
| Corpus name & link | Number of paraphrases |
|---|---|
| OpenSubtitles | 18,764,810 |
| WikiMatrix v1 | 1,569,231 |
| Tatoeba v2022-03-03 | 313,105 |
| TED2020 v1 | 289,374 |
| News-Commentary v16 | 285,722 |
| GlobalVoices v2018q4 | 70,547 |
| sum | . 21,292,789 |
To-do
- add copyright info of individual datasets
- add column description
- upload dataset
Back translation
We have made the back translation from English to German with the help of Fairseq.
We used the transformer.wmt19.en-de model for this purpose:
en2de = torch.hub.load(
"pytorch/fairseq",
"transformer.wmt19.en-de",
checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt",
tokenizer="moses",
bpe="fastbpe",
)
Citations & Acknowledgements
OpenSubtitles\
- citation: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
- also see http://www.opensubtitles.org/
WikiMatrix v1\
- citation: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia, arXiv, July 11 2019
Tatoeba v2022-03-03, News-Commentary v16 & GlobalVoices v2018q4\
- citation: J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
TED2020 v1\
- citation: Reimers, Nils and Gurevych, Iryna, Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020
- acknowledgements to OPUS for this service
Licensing
Copyright (c) 2022 Philip May, Deutsche Telekom AG
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file LICENSE in the repository.