Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Luxembourgish
Size:
10K - 100K
License:
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/) | |
## 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" | |
} | |
``` | |