The Datasets of Human and AI Translation

Yoke Lian Lau; Shiaw Phin Chee; Ruth Hui Hui Chua; Zi Hong Yong; Ing Ket Yong; Jee Chin Tan; Hui Wen Yong; Anna Lynn Abu Bakar

doi:10.5334/johd.212

Abstract

The datasets outline a methodical approach for comparing translations from Mandarin to Malay completed by humans and AI. The datasets contain a framework with rubrics for a keyword detection template to discover common words used by both humans and AI in their translation tasks. The datasets include a Mandarin poem originally written in Mandarin, along with translations by a belt-and-road project translator, a Mandarin native speaker translator, and a Malay native speaker translator categorised as human translators. It also contains translations by ChatGPT 3.5 with 1 prompt, and ChatGPT 4.0 with four different prompts. All these prompts are also listed in the dataset.

References

1Almufareh, M. F., Tehsin, S., Humayun, M., & Kausar, S. (2023). Intellectual disability and technology: An artificial intelligence perspective and framework. Journal of Disability Research, 2(4). DOI: 10.57197/jdr-2023-0055
Back to article
2Anahita, M. B. (2015). Word Alignment for Statistical Machine Translation Using Hidden Markov Models. [Report, Simon Fraser University].
Back to article
3Anuraj, B., & Goutam, M. (2024). A case study on tools and techniques of machine translation of Indian low resource languages. In P. Pakray, P. Dadure & S. Bandyopadhyay (Eds.), Empowering low-resource languages with NLP solutions (pp. 51–85). IGI Global. DOI: 10.4018/979-8-3693-0728-1.ch004
Back to article
4Bakarola, V., & Nasriwala, J. (2021). Attention-based neural machine translation approach for low-resourced Indic languages—A case of Sanskrit to Hindi translation. Smart Systems: Innovations in Computing, 565–572. DOI: 10.1007/978-981-16-2877-1_52
Back to article
5Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. DOI: 10.1016/j.caeai.2023.100198
Back to article
6Kuang, H., Chen, H., Ma, X., & Liu, X. (2022). A keyword detection and context filtering method for document level relation extraction. Applied Sciences, 12(3), 1599. DOI: 10.3390/app12031599
Back to article
7Kumar, R., & Sahula, V. (2022). Word translation using cross-lingual word embedding: Case of Sanskrit to Hindi translation. In 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP). DOI: 10.1109/aisp53593.2022.9760564
Back to article
8Lau, Y. L., Yong, Z. X., Chia, C. E., Yong, Z. H., Abu Bakar, A. L., Ku, C. J., Nasir, E., & Arumugam, B. (2024). Comparing translation accuracy in Belt and Road Malaysia children’s literature: Malay and Chinese native speakers vs ChatGPT. Forum for Linguistic Studies, 6(1), 2069. Retrieved from https://ojs.acad-pub.com/index.php/FLS/article/view/v6i1.2069. DOI: 10.59400/fls.v6i1.2069
Back to article
9Lau, Y. L. (2024a). Poem human and AI translator [Data set]. Mendeley Data. DOI: 10.17632/vc5wc8rymx.1
Back to article
10Lau, Y. L. (2024b). Rubric and template [Data set]. Mendeley Data. DOI: 10.17632/s6bx8wyvwg.1
Back to article
11Li, X. (2024). Comparison of translation quality between large language models and neural machine translation systems: A case study of Chinese-English language pair. International Journal of Education and Humanities, 4(2), 121–128. DOI: 10.58557/(ijeh).v4i2.213
Back to article
12Lin, F., & Mitamura, T. (2004). Keyword translation from English to Chinese for multilingual QA. Machine Translation: From Real Users to Research, 164–176. DOI: 10.1007/978-3-540-30194-3_19
Back to article
13Lin, Y. (2023). The relationship between machine translation and human translation in the era of artificial intelligence machine translation. Applied and Computational Engineering, 5(1), 133–138. DOI: 10.54254/2755-2721/5/20230547
Back to article
14Liu, Y., & Liang, J. (2024). Multidimensional comparison of Chinese-English interpreting outputs from human and machine: Implications for interpreting education in the machine-translation age. Linguistics and Education, 80, 101273. DOI: 10.1016/j.linged.2024.101273
Back to article
15Kelebercová, L., & Forgac, F. (2022). Keyword extraction for automatic evaluation of machine translation.
Back to article
16McKellar, C. A., & Puttkammer, M. J. (2020). Dataset for comparable evaluation of machine translation between 11 South African languages. Data in Brief, 29, 105146. DOI: 10.1016/j.dib.2020.105146
Back to article
17Mohammed Moneus, A., & Sahari, Y. (2023). Artificial intelligence and human translation: A contrastive study based on legal texts. DOI: 10.2139/ssrn.4441379
Back to article
18Sun, W. (2021). Integration of machine translation and manual translation in translation practice based on artificial intelligence and big data technology. In 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (pp. 1895–1897). DOI: 10.1145/3495018.3495510
Back to article
19Sundberg, L., & Holmström, J. (2024). Innovating by prompting: How to facilitate innovation in the age of generative AI. Business Horizons. DOI: 10.1016/j.bushor.2024.04.014
Back to article
20Nekoto, W., Marivate, V., Matsila, T., Fasubaa, T., Kolawole, T., Fagbohungbe, T., Akinola, S. O., Muhammad, S. H., Kabongo, S., Osei, S., Freshia, S., Niyongabo, R. A., Macharm, R., Ogayo, P., Ahia, O., Meressa, M., Adeyemi, M., Mokgesi-Selinga, M., Okegbemi, L., … Martinus, L. J. (2020). Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages. Findings of the Association for Computational Linguistics: EMNLP, 2144–2160. DOI: 10.18653/v1/2020.findings-emnlp.195
Back to article
21Wong, B., & Kit, C. (2009). ATEC: Automatic evaluation of machine translation via word choice and word order. Machine Translation, 23(2–3), 141–155. DOI: 10.1007/s10590-009-9061-x
Back to article
22Yu, Y. X. (2024). Application of translation technology based on AI in translation teaching. Systems and Soft Computing, 6, 200072. DOI: 10.1016/j.sasc.2024.200072
Back to article
23Zhili, W., & Qian, Z. (2024). A deep learning-based method for determining semantic similarity of English translation keywords. International Journal of Advanced Computer Science and Applications, 15(5). DOI: 10.14569/ijacsa.2024.0150531
Back to article

The Datasets of Human and AI Translation

Abstract

Paradigm

My account