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---
task_categories:
- translation
language:
- it
- lld
size_categories:
- n<1K
---
# Dataset Card: Testset 1
## Overview
**Dataset Name**: Testset 1
**Source Paper**: ["Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin"](https://arxiv.org/abs/2407.08819)
**Description**:
Testset 1 consists of parallel sentences in Ladin and Italian.
## Dataset Structure
- **Files**:
- `statut.parquet`: Contains the Italian - Ladin (Val Badia) translations.
## Format
- **File Type**: Parquet
- **Encoding**: UTF-8
## Usage
```python
from datasets import load_dataset
data = load_dataset("sfrontull/stiftungsparkasse-lld_valbadia-ita")
```
## Citation
If you use this dataset, please cite the following paper:
```bibtex
@inproceedings{frontull-moser-2024-rule,
title = "Rule-Based, Neural and {LLM} Back-Translation: Comparative Insights from a Variant of {L}adin",
author = "Frontull, Samuel and
Moser, Georg",
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.loresmt-1.13",
pages = "128--138",
abstract = "This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.",
}
```
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