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--- |
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language: |
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- ca |
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- de |
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multilinguality: |
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- multilingual |
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pretty_name: CA-DE Parallel Corpus |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- translation |
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task_ids: [] |
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license: cc-by-nc-sa-4.0 |
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--- |
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# Dataset Card for CA-DE Parallel Corpus |
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## Dataset Description |
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- **Point of Contact:** [email protected] |
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### Dataset Summary |
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The CA-DE Parallel Corpus is a Catalan-German dataset of parallel sentences created to support Catalan in NLP tasks, specifically |
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Machine Translation. |
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### Supported Tasks and Leaderboards |
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The dataset can be used to train Bilingual Machine Translation models between German and Catalan in any direction, |
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as well as Multilingual Machine Translation models. |
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### Languages |
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The sentences included in the dataset are in Catalan (CA) and German (DE). |
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## Dataset Structure |
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### Data Instances |
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Two separate txt files are provided with the sentences sorted in the same order: |
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- ca-de_all_2023_09_11.ca |
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- ca-de_all_2023_09_11.de |
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The dataset is additionally provided in parquet format: ca-de_all_2023_09_11.parquet. |
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The parquet file contains two columns of parallel text obtained from the two original text files. |
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Each row in the file represents a pair of parallel sentences in the two languages of the dataset. |
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### Data Fields |
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[N/A] |
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### Data Splits |
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The dataset contains a single split: `train`. |
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## Dataset Creation |
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### Curation Rationale |
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This dataset is aimed at promoting the development of Machine Translation between Catalan and other languages, specifically German. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The first portion of the corpus is a combination of the following original datasets collected from [Opus](https://opus.nlpl.eu/): |
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MultiCCAligned, WikiMatrix, GNOME, KDE4, OpenSubtitles, GlobalVoices, Tatoeba. |
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Additionally, the corpus contains synthetic parallel data generated from the original Spanish-Catalan Europarl and Tilde corpora |
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made public by [SoftCatalà](https://github.com/Softcatala/Europarl-catalan). |
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A last portion of the dataset is composed by synthetic parallel data generated from a random sampling of the Spanish-German corpora |
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available on [Opus](https://opus.nlpl.eu/) and translated into Catalan using the [PlanTL es-ca](https://huggingface.co/PlanTL-GOB-ES/mt-plantl-es-ca) model. |
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All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. |
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This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). |
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The filtered datasets are then concatenated to form the final corpus. |
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#### Who are the source language producers? |
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[Opus](https://opus.nlpl.eu/) |
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[SoftCatalà](https://github.com/Softcatala/Europarl-catalan) |
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### Annotations |
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#### Annotation process |
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The dataset does not contain any annotations. |
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#### Who are the annotators? |
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[N/A] |
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### Personal and Sensitive Information |
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Given that this dataset is partly derived from pre-existing datasets that may contain crawled data, and that no specific anonymisation process has been applied, |
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personal and sensitive information may be present in the data. This needs to be considered when using the data for training models. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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By providing this resource, we intend to promote the use of Catalan across NLP tasks, thereby improving the accessibility and visibility of the Catalan language. |
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### Discussion of Biases |
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No specific bias mitigation strategies were applied to this dataset. |
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Inherent biases may exist within the data. |
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### Other Known Limitations |
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The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use. |
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## Additional Information |
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### Dataset Curators |
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Language Technologies Unit at the Barcelona Supercomputing Center ([email protected]). |
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This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). |
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### Licensing Information |
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This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
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### Citation Information |
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[N/A] |
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### Contributions |
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[N/A] |
