|
|
--- |
|
|
license: cc-by-4.0 |
|
|
task_categories: |
|
|
- text-classification |
|
|
- automatic-speech-recognition |
|
|
- translation |
|
|
language: |
|
|
- wo |
|
|
- fr |
|
|
- en |
|
|
pretty_name: WolBanking77 |
|
|
size_categories: |
|
|
- 1K<n<10K |
|
|
tags: |
|
|
- arxiv:2509.19271 |
|
|
--- |
|
|
|
|
|
Intent classification models have made a lot of progress in recent years. However, previous studies primarily focus on high-resource languages datasets, which results in a gap for low-resource languages and for regions with a high rate of illiterate people where languages are more spoken than read or written. This is the case in Senegal, for example, where Wolof is spoken by around 90% of the population, with an illiteracy rate of 42% for the country. Wolof is actually spoken by more than 10 million people in West African region. |
|
|
|
|
|
To tackle such limitations, we release a Wolof Banking Speech Intent Classification Dataset (**WolBanking77**), for academic research in intent classification. WolBanking77 currently contains **9,791** text sentences in the **banking domain** and more than **4** hours of spoken sentences. |
|
|
|
|
|
This dataset is suitable for : |
|
|
|
|
|
- Customer Intent Detection |
|
|
- Machine Translation in French & Wolof |
|
|
- Automatic Speech Recognition in Wolof |
|
|
- Comparing different machine learning models for Intent Classification |
|
|
|
|
|
# **Dataset Content**: |
|
|
- **text** dir - Contains text dataset |
|
|
- **audio** dir - Contains audio dataset |
|
|
- **train.csv** – Text Training dataset (includes label target) |
|
|
- **test.csv** – Text Test dataset (no label target) |
|
|
- **train.parquet** - Audio Training dataset |
|
|
- **test.parquet** - Audio Test dataset |
|
|
- **questions.csv** - Customer queries |
|
|
- **responses.csv** - Bot responses |
|
|
|
|
|
### **Target Variable**: |
|
|
|
|
|
- **label**→ The client intent |
|
|
|
|
|
### **Features**: |
|
|
|
|
|
- `input` – Client’s query in English (text) |
|
|
- `input_fr` – Client’s query in French (text) |
|
|
- `input_wo` – Client’s query in Wolof (text) |
|
|
- `label` – Client's Intent (categorical) |
|
|
|
|
|
# **License** |
|
|
**CC BY 4.0** – Open for public use. |
|
|
|
|
|
# **Inspiration** |
|
|
- Can you predict the client intent? |
|
|
- How well do different machine learning models perform on this classification task? |
|
|
|
|
|
# Citation |
|
|
|
|
|
To cite this work, please use the following reference: |
|
|
|
|
|
```bibtex |
|
|
@inproceedings{ |
|
|
kandji2025wolbanking, |
|
|
title={WolBanking77: Wolof Banking Speech Intent Classification Dataset}, |
|
|
author={Abdou Karim KANDJI and Frederic Precioso and Cheikh BA and Samba NDIAYE and Augustin NDIONE}, |
|
|
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
|
|
year={2025}, |
|
|
url={https://openreview.net/forum?id=7k0JBDeHAv} |
|
|
} |
|
|
``` |
|
|
```bibtex |
|
|
@misc{ |
|
|
kandji2025wolbanking77wolofbankingspeech, |
|
|
title={WolBanking77: Wolof Banking Speech Intent Classification Dataset}, |
|
|
author={Abdou Karim Kandji and Frédéric Precioso and Cheikh Ba and Samba Ndiaye and Augustin Ndione}, |
|
|
year={2025}, |
|
|
eprint={2509.19271}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL}, |
|
|
url={https://arxiv.org/abs/2509.19271}, |
|
|
} |
|
|
``` |
