README.md

metadata

license:
  - cc-by-sa-4.0
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.

Columns description

xxx

Load this dataset with Pandas

If you want to download the csv file and then load it with Pandas you can do it like this:

df = pd.read_csv("train.csv")

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 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/
• license: no special license has been provided at OPUS for this dataset

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
• license: CC-BY-SA 4.0

Tatoeba v2022-03-03

• 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)
• license: CC BY 2.0 FR
• copyright: https://tatoeba.org/eng/terms_of_use

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
• license: please respect the TED Talks Usage Policy

News-Commentary v16

• 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)
• license: no special license has been provided at OPUS for this dataset

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)
• license: no special license has been provided at OPUS for this dataset

Licensing

Copyright (c) 2022 Philip May, Deutsche Telekom AG

This work is licensed under CC-BY-SA 4.0.