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

• uuid: xxx
• de: xxx
• en_de: xxx
• corpus: xxx
• min_char_len: xxx
• jaccard_similarity: xxx
• de_token_count: xxx
• en_de_token_count: xxx
• cos_sim: 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.