SEACrowd/id_wiki_parallel

This dataset is designed for machine translation task, specifically jav->ind, min->ind, sun->ind, and vice versa. The data are taken from sentences in Wikipedia.

(from the publication abstract) Parallel corpora are necessary for multilingual researches especially in information retrieval (IR) and natural language processing (NLP). However, such corpora are hard to find, specifically for low-resources languages like ethnic languages. Parallel corpora of ethnic languages were usually collected manually. On the other hand, Wikipedia as a free online encyclopedia is supporting more and more languages each year, including ethnic languages in Indonesia. It has become one of the largest multilingual sites in World Wide Web that provides free distributed articles. In this paper, we explore a few sentence alignment methods which have been used before for another domain. We want to check whether Wikipedia can be used as one of the resources for collecting parallel corpora of Indonesian and Javanese, an ethnic language in Indonesia. We used two approaches of sentence alignment by treating Wikipedia as both parallel corpora and comparable corpora. In parallel corpora case, we used sentence length based and word correspondence methods. Meanwhile, we used the characteristics of hypertext links from Wikipedia in comparable corpora case. After the experiments, we can see that Wikipedia is useful enough for our purpose because both approaches gave positive results.

Languages

ind, jav, min, sun

Supported Tasks

Machine Translation

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/id_wiki_parallel", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("id_wiki_parallel", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("id_wiki_parallel"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://github.com/dindainastra/indowikiparalelcorpora

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Unknown

Citation

If you are using the Id Wiki Parallel dataloader in your work, please cite the following:

@INPROCEEDINGS{
7065828,
author={Trisedya, Bayu Distiawan and Inastra, Dyah},
booktitle={2014 International Conference on Advanced Computer Science and Information System},
title={Creating Indonesian-Javanese parallel corpora using wikipedia articles},
year={2014},
volume={},
number={},
pages={239-245},
doi={10.1109/ICACSIS.2014.7065828}}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}