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Datasets:
fredxlpy
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LuxAlign_v1

Tasks:
Sentence Similarity
Modalities:
Text
Formats:
json
Languages:
Luxembourgish
Luxembourgish
Size:
100K - 1M
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metadata
license: cc-by-nc-4.0
task_categories:
  - sentence-similarity
language:
  - lb
  - ltz
size_categories:
  - 10K<n<100K
configs:
  - config_name: lb-en
    data_files:
      - split: train
        path: lb_en.json
  - config_name: lb-fr
    data_files:
      - split: train
        path: lb_fr.json

Dataset Card for LuxAlign

Updates

⚠️ A newer version of this dataset is available.
Although this is the original dataset described in the paper, a revised version of the dataset is available at https://huggingface.co/datasets/fredxlpy/LuxAlign.

Dataset Summary

LuxAlign is a parallel dataset featuring Luxembourgish-English and Luxembourgish-French sentence pairs, introduced in LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings (Philippy et al., 2024). Designed to align the Luxembourgish embedding space with those of other languages, it enables improved cross-lingual sentence representations for Luxemborgish. This dataset was used to train the Luxembourgish sentence embedding model LuxEmbedder. The data originates from news articles published by the Luxembourgish news platform RTL.lu.

The sentence pairs in this dataset are not always exact translations but instead reflect high semantic similarity; hence, this dataset may not be suitable for training a machine translation model without caution.

Dataset Description

Citation Information 

@misc{philippy2024,
      title={LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings}, 
      author={Fred Philippy and Siwen Guo and Jacques Klein and Tegawendé F. Bissyandé},
      year={2024},
      eprint={2412.03331},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.03331}, 
}

We would like to express our sincere gratitude to RTL Luxembourg for providing the raw seed data that served as the foundation for this research. Those interested in obtaining this data are encouraged to reach out to RTL Luxembourg or Mr. Tom Weber via ai@rtl.lu.