Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Luxembourgish
Size:
10K - 100K
License:
File size: 3,441 Bytes
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---
license: cc-by-4.0
task_categories:
- text-classification
language:
- lb
size_categories:
- 10K<n<100K
#source_datasets:
#- Luxembourg Online Dictionary (LOD)
configs: # Optional. This can be used to pass additional parameters to the dataset loader, such as `data_files`, `data_dir`, and any builder-specific parameters
- config_name: LETZ-SYN # Example: default
data_files:
- split: train
path: LETZ-SYN/train.json
- split: validation
path: LETZ-SYN/val.json
- split: test
path: LETZ-SYN/test.json
- config_name: LETZ-WoT # Example: default
data_files:
- split: train
path: LETZ-WoT/train.json
- split: validation
path: LETZ-WoT/val.json
- split: test
path: LETZ-WoT/test.json
---
# Dataset Card for Luxembourgish Entailment-based Topic classification via Zero-shot learning (LETZ)
## Dataset Summary
The datasets for **L**uxembourgish **E**ntailment-based **T**opic classification via **Z**ero-shot learning (**LETZ**) can be used to adapt language models to zero-shot classification in Luxembourgish. It leverages data from the [*Luxembourg Online Dictionary*](https://lod.lu) to provide relevant topic classification examples in Luxembourgish. The LETZ datasets were created to address the limitations of using Natural Language Inference (NLI) datasets for zero-shot classification in low-resource languages. Specifically, they aim to improve topic classification performance by providing more relevant and accessible data through dictionary entries.
## Columns in the Dataset
Each dataset includes the following columns:
* **Text**: The Luxembourgish sentence or phrase.
* **Label**: The potentially associated topic label.
* **Class**: A binary indicator where “1” denotes relevance (entailment) and “0” denotes irrelevance (non-entailment).
## Dataset Description
- **Repository:** [fredxlpy/LETZ](https://github.com/fredxlpy/LETZ)
- **Paper:** [Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to Luxembourgish (Philippy et al., 2024)](https://aclanthology.org/2024.sigul-1.13/)
- **Source Data** [Luxembourg Online Dictionary](https://data.public.lu/en/datasets/letzebuerger-online-dictionnaire-lod-linguistesch-daten/)
## Source Data
The original [Luxembourg Online Dictionary](https://lod.lu) (LOD) data can be downloaded from the [Luxembourgish Open Data Platform](https://data.public.lu/fr/datasets/letzebuerger-online-dictionnaire-lod-linguistesch-daten/) or can be accessed via their [API](https://data.public.lu/fr/datasets/letzebuerger-online-dictionnaire-lod-public-api/). All of their data is available under a [Creative Commons Zero (CC0)](https://creativecommons.org/publicdomain/zero/1.0/) license.
## Citation Information
```
@inproceedings{philippy-etal-2024-forget,
title = "Forget {NLI}, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to {L}uxembourgish",
author = "Philippy, Fred and
Haddadan, Shohreh and
Guo, Siwen",
editor = "Melero, Maite and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.sigul-1.13",
pages = "97--104"
}
```
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