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--- |
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license: |
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- cc-by-sa-4.0 |
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language: |
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- de |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 10M<n<100M |
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task_categories: |
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- sentence-similarity |
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--- |
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**This dataset card is still a draft version. The dataset has not been uploaded yet.** |
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This is a record of German language paraphrases. These are text pairs that have the same meaning but are expressed in different words. |
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The source of the paraphrases are different parallel German / English text corpora. |
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The English texts were machine translated back into German. This is how the paraphrases were obtained. |
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## To-do |
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- upload dataset |
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- explain out preprocessing |
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- suggest further postprocessing |
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- explain dirty "texts" in OpenSubtitles |
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## Columns description |
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- **`uuid`**: a uuid calculated with Python `uuid.uuid4()` |
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- **`de`**: the original German texts from the corpus |
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- **`en_de`**: the German texts translated back from English |
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- **`corpus`**: the name of the corpus |
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- **`min_char_len`**: the number of characters of the shortest text |
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- **`jaccard_similarity`**: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences - see below for more details |
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- **`de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) |
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- **`en_de_token_count`**: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) |
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- **`cos_sim`**: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) |
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## Parallel text corpora used |
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| Corpus name & link | Number of paraphrases | |
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|-----------------------------------------------------------------------|----------------------:| |
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| [OpenSubtitles](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | 18,764,810 | |
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| [WikiMatrix v1](https://opus.nlpl.eu/WikiMatrix-v1.php) | 1,569,231 | |
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| [Tatoeba v2022-03-03](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php) | 313,105 | |
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| [TED2020 v1](https://opus.nlpl.eu/TED2020-v1.php) | 289,374 | |
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| [News-Commentary v16](https://opus.nlpl.eu/News-Commentary-v16.php) | 285,722 | |
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| [GlobalVoices v2018q4](https://opus.nlpl.eu/GlobalVoices-v2018q4.php) | 70,547 | |
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| **sum** |. **21,292,789** | |
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## Back translation |
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We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq). |
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We used the `transformer.wmt19.en-de` model for this purpose: |
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```python |
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en2de = torch.hub.load( |
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"pytorch/fairseq", |
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"transformer.wmt19.en-de", |
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checkpoint_file="model1.pt:model2.pt:model3.pt:model4.pt", |
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tokenizer="moses", |
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bpe="fastbpe", |
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) |
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``` |
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## How the Jaccard similarity was calculated |
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To calculate the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) |
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we are using the [SoMaJo tokenizer](https://github.com/tsproisl/SoMaJo) |
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to split the texts into tokens. |
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We then `lower()` the tokens so that upper and lower case letters no longer make a difference. Below you can find a code snippet with the details: |
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```python |
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from somajo import SoMaJo |
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LANGUAGE = "de_CMC" |
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somajo_tokenizer = SoMaJo(LANGUAGE) |
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def get_token_set(text, somajo_tokenizer): |
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sentences = somajo_tokenizer.tokenize_text([text]) |
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tokens = [t.text.lower() for sentence in sentences for t in sentence] |
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token_set = set(tokens) |
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return token_set |
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def jaccard_similarity(text1, text2, somajo_tokenizer): |
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token_set1 = get_token_set(text1, somajo_tokenizer=somajo_tokenizer) |
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token_set2 = get_token_set(text2, somajo_tokenizer=somajo_tokenizer) |
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intersection = token_set1.intersection(token_set2) |
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union = token_set1.union(token_set2) |
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jaccard_similarity = float(len(intersection)) / len(union) |
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return jaccard_similarity |
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``` |
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## Load this dataset with Pandas |
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If you want to download the csv file and then load it with Pandas you can do it like this: |
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```python |
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df = pd.read_csv("train.csv") |
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``` |
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## Citations & Acknowledgements |
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**OpenSubtitles** |
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- citation: P. Lison and J. Tiedemann, 2016, [OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles](http://www.lrec-conf.org/proceedings/lrec2016/pdf/947_Paper.pdf). In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) |
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- also see http://www.opensubtitles.org/ |
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- license: no special license has been provided at OPUS for this dataset |
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**WikiMatrix v1** |
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- citation: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://arxiv.org/abs/1907.05791), arXiv, July 11 2019 |
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- license: [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
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**Tatoeba v2022-03-03** |
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- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) |
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- license: [CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/) |
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- copyright: https://tatoeba.org/eng/terms_of_use |
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**TED2020 v1** |
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- citation: Reimers, Nils and Gurevych, Iryna, [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813), In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, November 2020 |
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- acknowledgements to [OPUS](https://opus.nlpl.eu/) for this service |
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- license: please respect the [TED Talks Usage Policy](https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy) |
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**News-Commentary v16** |
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- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) |
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- license: no special license has been provided at OPUS for this dataset |
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**GlobalVoices v2018q4** |
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- citation: J. Tiedemann, 2012, [Parallel Data, Tools and Interfaces in OPUS](https://opus.nlpl.eu/Tatoeba-v2022-03-03.php). In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) |
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- license: no special license has been provided at OPUS for this dataset |
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## Licensing |
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Copyright (c) 2022 Philip May, Deutsche Telekom AG |
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This work is licensed under [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). |
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