Datasets:
license: cc-by-sa-4.0
task_categories:
- multiple-choice
language:
- id
size_categories:
- n<1K
configs:
- config_name: id
data_files:
- split: test
path: test_copal.csv
- split: test_colloquial
path: test_copal_colloquial.csv
About COPAL-ID
COPAL-ID is an Indonesian causal commonsense reasoning dataset that captures local nuances. It provides a more natural portrayal of day-to-day causal reasoning within the Indonesian (especially Jakartan) cultural sphere. Professionally written and validatid from scratch by natives, COPAL-ID is more fluent and free from awkward phrases, unlike the translated XCOPA-ID.
COPAL-ID is a test set only, intended to be used as a benchmark. For more details, please see our paper.
Local Nuances Categories
Our dataset consists of 3 subcategories: local-term, culture, and language reasoning.
- Local-term captures common knowledge for Indonesians that is most likely unknown or uncommon for non-natives, e.g., local foods, public figures, abbreviations, and other local concepts.
- Culture captures norms used in Indonesia.
- Language captures the reasoning for the language itself, for example, local idioms, figures of speech, as well as ambiguous words. Specifically, the distribution of COPAL-ID across these categories is:
Colloquial vs Standard Indonesian
In daily scenarios, almost no one in Indonesia uses purely formal Indonesian. Yet, many NLP datasets use formal Indonesian. This surely causes a domain mismatch with real-case settings. To accommodate this, COPAL-ID is written in two variations: Standard Indonesian and Colloquial Indonesian. If you use COPAL-ID to benchmark your model, we suggest testing on both variants. Generally, colloquial Indonesian is harder for models to handle.
How to Use
from datasets import load_dataset
copal_id_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test')
copal_id_colloquial_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test_colloquial')
Data Collection and Human Performance
COPAL-ID was created through a rigorous data collection pipeline. Each example is written and checked by natives accustomed to Jakartan culture. Lastly, we have run a human benchmark performance test across native Jakartans, in which they achieved an average accuracy of ~95% in both formal and colloquial Indonesian variants, noting that this dataset is trivially easy for those familiar with the culture and local nuances of Indonesia, especially in Jakarta.
For more details, please see our paper.
Limitation
Indonesia is a vast country with over 700+ languages and rich in culture. Therefore, it is impossible to pinpoint a singular culture. Our dataset is specifically designed to capture Jakarta's (the capital) local nuances. Expanding to different local nuances and languages across Indonesia is a future work.
Cite Our Work
@article{wibowo2023copal,
title={COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances},
author={Wibowo, Haryo Akbarianto and Fuadi, Erland Hilman and Nityasya, Made Nindyatama and Prasojo, Radityo Eko and Aji, Alham Fikri},
journal={arXiv preprint arXiv:2311.01012},
year={2023}
}