SEACrowd/indo_religious_mt_en_id

印尼宗教领域机器翻译数据集（Indo Religious Mt En Id）包含宗教手稿或文章，这些文章不同于新闻，它们以倡导和启发宗教价值为目的，常引用圣经或古兰经的轶事。数据集的一个有趣特性是本地化名称的使用，例如David到Daud，Mary到Maryam，Gabriel到Jibril等。此外，数据集中还存在一些翻译不一致的情况，例如praying有时被翻译为salat或shalat，repentance有时被翻译为tobat或taubat。

The Indonesian Religious Domain Machine Translation Dataset (Indo Religious Mt En Id) contains religious manuscripts and articles. Unlike news articles, these works are intended to promote and inspire religious values, and often cite anecdotes from the Bible or the Quran. An interesting feature of the dataset is the use of localized proper names: for example, David is translated as Daud, Mary as Maryam, Gabriel as Jibril, and so on. Additionally, there are some translation inconsistencies in the dataset: for instance, the term "praying" is sometimes translated as "salat" or "shalat", and "repentance" is sometimes rendered as "tobat" or "taubat".

原始信息汇总

数据集概述

数据集名称

Indo Religious Mt En Id

语言

• 印度尼西亚语 (ind)
• 英语 (eng)

任务类别

机器翻译

标签

机器翻译

数据集描述

Indonesian Religious Domain MT En-Id 包含宗教手稿或文章。这些文章与新闻不同，不是正式的、信息性的风格，而是旨在倡导和激发宗教价值观，经常引用圣经或古兰经的轶事。宗教领域语料库的一个有趣特性是本地化名称，例如 David 到 Daud，Mary 到 Maryam，Gabriel 到 Jibril 等。与其他领域相比，实体名称通常保持不变。我们还发现，JW300 的许多印度尼西亚语翻译缺少句末句点（.），尽管它们的英语对应部分有句末句点。在音译中也发现了一些不一致之处，例如祈祷有时写作 "salat" 或 "shalat"，或忏悔写作 "tobat" 或 "taubat"。

数据集版本

• 源版本：1.0.0
• SEACrowd 版本：2024.06.20

数据集许可证

Creative Commons Attribution Share-Alike 4.0 International

引用

如果使用 Indo Religious Mt En Id 数据加载器，请引用以下内容：

@inproceedings{guntara-etal-2020-benchmarking, title = "Benchmarking Multidomain {E}nglish-{I}ndonesian Machine Translation", author = "Guntara, Tri Wahyu and Aji, Alham Fikri and Prasojo, Radityo Eko", booktitle = "Proceedings of the 13th Workshop on Building and Using Comparable Corpora", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.bucc-1.6", pages = "35--43", abstract = "In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on https://github.com/gunnxx/indonesian-mt-data.", language = "English", ISBN = "979-10-95546-42-9", }

@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} }