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
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
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
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
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.