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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- multiple-choice |
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language: |
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- id |
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size_categories: |
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- n<1K |
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configs: |
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- config_name: id |
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data_files: |
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- split: test |
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path: test_copal.csv |
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- split: test_colloquial |
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path: test_copal_colloquial.csv |
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--- |
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## About COPAL-ID |
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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. |
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COPAL-ID is a test set only, intended to be used as a benchmark. |
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For more details, please see [our paper](https://arxiv.org/abs/2311.01012). |
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### Local Nuances Categories |
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Our dataset consists of 3 subcategories: local-term, culture, and language reasoning. |
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- 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. |
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- Culture captures norms used in Indonesia. |
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- Language captures the reasoning for the language itself, for example, local idioms, figures of speech, as well as ambiguous words. |
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Specifically, the distribution of COPAL-ID across these categories is: |
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### Colloquial vs Standard Indonesian |
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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. |
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## How to Use |
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```py |
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from datasets import load_dataset |
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copal_id_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test') |
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copal_id_colloquial_dataset = load_dataset('haryoaw/COPAL', 'id', subset='test_colloquial') |
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``` |
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## Data Collection and Human Performance |
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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. |
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For more details, please see our paper. |
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## Limitation |
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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. |
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## Cite Our Work |
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``` |
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@article{wibowo2023copal, |
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title={COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances}, |
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author={Wibowo, Haryo Akbarianto and Fuadi, Erland Hilman and Nityasya, Made Nindyatama and Prasojo, Radityo Eko and Aji, Alham Fikri}, |
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journal={arXiv preprint arXiv:2311.01012}, |
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year={2023} |
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} |
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``` |
