Multilingualism, Translation, and Language Technologies in Multicultural and Low-Resource Contexts: A Systematic Literature Review

The increasing interconnectedness of global societies has intensified the importance of multilingualism, translation, and language technologies in facilitating communication across diverse linguistic and cultural contexts. Simultaneously, advances in artificial intelligence and natural language processing have accelerated the development of language technologies aimed at addressing challenges associated with multilingual and low-resource language environments. Despite growing scholarly attention, research in these areas remains fragmented across disciplines such as sociolinguistics, translation studies, language education, and computational linguistics. Therefore, this systematic literature review (SLR) aims to synthesize current knowledge and identify emerging trends related to multilingualism, translation, and language technologies in multicultural and low-resource contexts. Guided by the PRISMA framework and informed by the PICo approach, the review analyzed 38 peer-reviewed studies published between 2021 and 2026 retrieved from the Scopus and Web of Science databases. The findings revealed four major themes: (1) Multilingualism, Language Identity, and Language Learning; (2) Translation, Transliteration, and Cross-Cultural Communication; (3) Low-Resource Language Technologies and Machine Translation Innovation; and (4) Sociocultural, Historical, and Applied Language Studies. The review highlights the continued significance of multilingual practices in identity formation and language learning, the role of translation in mediating intercultural communication, and the growing influence of artificial intelligence in improving accessibility for low-resource languages. Furthermore, the findings indicate a research imbalance, with technology-focused studies dominating the literature while sociocultural and historical perspectives remain comparatively underexplored. This review contributes to the development of an integrated understanding of language, culture, and technology and offers directions for future research and practice in multilingual and low-resource settings.

Multilingualism, Translation and Transliteration, Language Technologies, Low-Resource Languages, Cross-cultural Communication

1. Abouzahra, A., Sabraoui, A., & Afdel, K. (2020). Model composition in Model Driven Engineering: A systematic literature review. Information and Software Technology, 125, 106316. https://doi.org/10.1016/j.infsof.2020.106316 [Google Scholar] [Crossref]

2. Bang, J. U., Maeng, J. G., Park, J., Yun, S., & Kim, S. H. (2023). English-Korean speech translation corpus (EnKoST-C): Construction procedure and evaluation results. ETRI Journal, 45(1), 18–27. https://doi.org/10.4218/etrij.2021-0336 [Google Scholar] [Crossref]

3. Bei, C., Zong, H., Liu, Q. M., & Yuan, C. H. (2021). GTCOM neural machine translation systems for WMT21. In Proceedings of the Sixth Conference on Machine Translation (pp. 100-103). Association for Computational Linguistics. [Google Scholar] [Crossref]

4. Berger, P. L., & Luckmann, T. (1966). The social construction of reality: A treatise in the sociology of knowledge. Anchor Books. [Google Scholar] [Crossref]

5. Choi, H. W., & Ko, G. H. (2023). Orthographic influence on resyllabification errors by Vietnamese learners of Korean: A speech corpus study. Linguistic Research, 40(Special Edition), 33–59. https://doi.org/10.17250/khisli.40..202309.002 [Google Scholar] [Crossref]

6. Chow, Y. F. (2024). Impact caption translation on a streaming media platform: The case of a Chinese reality show. Perspectives: Studies in Translation Theory and Practice, 32(1), 154–173. https://doi.org/10.1080/0907676X.2022.2162942 [Google Scholar] [Crossref]

7. Das, S., Chaudhuri, S., Saha, D., Das, D., & Bandyopadhyay, S. (2025). IWSLT 2025 Indic track system description paper: Speech-to-text translation from low-resource Indian languages (Bengali and Tamil) to English. In Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025) (pp. 245–251). [Google Scholar] [Crossref]

8. Ding, S. L., & Goh, K. L. (2024). Translingual practice as a representation of heritage languages and regional identities in multilingual society. Applied Linguistics Review, 15(6), 2453–2474. https://doi.org/10.1515/applirev-2022-0195 [Google Scholar] [Crossref]

9. Dinh, D., & Thanh, N. L. (2022). English–Vietnamese cross-language paraphrase identification using hybrid feature classes. Journal of Heuristics, 28(2), 193–209. https://doi.org/10.1007/s10732-019-09411-2 [Google Scholar] [Crossref]

10. Gopalakrishnan, R., & Sudeep, P. V. (2025). Photo-realistic image generation using text descriptions in Indian languages: An exploration study. Sādhanā, 50(4). https://doi.org/10.1007/s12046-025-02946-0 [Google Scholar] [Crossref]

11. Gu, C. (2023). A tale of three global cities: A comparative account of Dubai, Kuala Lumpur and Hong Kong's multilingual repertoires evidenced in their Covidscapes as part of Covid-19 crisis and public health communication. Language and Health, 1(2), 51–69. https://doi.org/10.1016/j.laheal.2023.06.001 [Google Scholar] [Crossref]

12. Gu, C., & Coluzzi, P. (2025). Presence of ‘ARABIC’ in Kuala Lumpur’s multilingual linguistic landscape: Heritage, religion, identity, business and mobility. International Journal of Multilingualism, 22(3), 1473–1503. https://doi.org/10.1080/14790718.2024.2356215 [Google Scholar] [Crossref]

13. Hardini, T. I., Yulianeta, Y., Fikrianto, M., Hamidah, H., Emilia, E., & Kharismawati, L. R. S. (2023). ASEAN students' interest in learning the Indonesian language: A descriptive study from the perspective of SEAMEO ASEAN. International Journal of Language Education, 7(4), 746–762. https://doi.org/10.26858/ijole.v7i4.59491 [Google Scholar] [Crossref]

14. Hlaing, Z. Z., Thu, Y. K., Supnithi, T., & Netisopakul, P. (2022). Improving neural machine translation with POS-tag features for low-resource language pairs. Heliyon, 8(8), e10375. https://doi.org/10.1016/j.heliyon.2022.e10375 [Google Scholar] [Crossref]

15. Hu, H., & Kübler, S. (2021). Investigating translated Chinese and its variants using machine learning. Natural Language Engineering, 27(3), 339–372. https://doi.org/10.1017/S1351324920000182 [Google Scholar] [Crossref]

16. Islam, M. A., Anik, M. S. H., & Al Islam, A. B. M. A. (2021). Towards achieving a delicate blending between rule-based translator and neural machine translator. Neural Computing and Applications, 33(18), 12141–12167. https://doi.org/10.1007/s00521-021-05895-x [Google Scholar] [Crossref]

17. Kitchenham, B. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3). EBSE Technical Report. [Google Scholar] [Crossref]

18. Koehn, P. (2010). Statistical Machine Translation. Cambridge: Cambridge University Press. [Google Scholar] [Crossref]

19. Koh, S. Y., Hoon, C.-Y., & Haji-Othman, N. A. (2021). “Mandarin fever” and Chinese language-learning in Brunei’s middle schools: Discrepant discourses, multifaceted realities and institutional barriers. Asian Studies Review, 45(2), 325–344. https://doi.org/10.1080/10357823.2020.1801577 [Google Scholar] [Crossref]

20. Kunchukuttan, A., Jain, S., & Kejriwal, R. (2021). A large-scale evaluation of neural machine transliteration for Indic languages. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021) (pp. 3469–3475). Association for Computational Linguistics. [Google Scholar] [Crossref]

21. Laamann, L. P. (2022). The Protestant missions to South-East Asia: Experimental laboratory of missionary concepts and of human relations (circa 1780–1840). Exchange, 51(3), 266–286. https://doi.org/10.1163/1572543X-BJA10005 [Google Scholar] [Crossref]

22. Lau, Y. L., Yong, Z. X., Chia, C. E., Yong, Z. H., Bakar, A. L. A., 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), Article 2069. https://doi.org/10.59400/fls.v6i1.2069 [Google Scholar] [Crossref]

23. Lee, S. T., van Heuven, W. J. B., Price, J. M., & Leong, C. X. R. (2023). Translation norms for Malay and English words: The effects of word class, semantic variability, lexical characteristics, and language proficiency on translation. Behavior Research Methods, 55(7), 3585–3601. https://doi.org/10.3758/s13428-022-01977-3 [Google Scholar] [Crossref]

24. Lockwood, C., Munn, Z., & Porritt, K. (2015). Qualitative research synthesis: Methodological guidance for systematic reviewers utilizing meta-aggregation. International Journal of Evidence-Based Healthcare, 13(3), 179–187. https://doi.org/10.1097/XEB.0000000000000062 [Google Scholar] [Crossref]

25. Mohammad Sauffi, K., Mohd Sani, M., & Senom, F. (2026). Translanguaging and identity preservation in English language learning: Multilinguals’ voices from a Malaysian university. International Journal of Multilingualism. Advance online publication. https://doi.org/10.1080/14790718.2026.2619880 [Google Scholar] [Crossref]

26. Mukundan, J. (2023). A review of materials development in multi-cultural developing world L2 contexts. Journal of Asia TEFL, 20(3), 507–523. https://doi.org/10.18823/asiatefl.2023.20.3.1.507 [Google Scholar] [Crossref]

27. Musaev, T., Aziz, J., & Mohd, J. (2021). Translation and linguistic strategies of English oxymora into Japanese by Japanese language learners. GEMA Online Journal of Language Studies, 21(4), 234–257. https://doi.org/10.17576/gema-2021-2104-12 [Google Scholar] [Crossref]

28. Nah, V. E. M. Y., Cavallaro, F., Panović, I., & Ng, B. C. (2021). Multilingualism among the elderly Chinese in Singapore: An oral account. International Journal of the Sociology of Language, 2021(270), 153–179. https://doi.org/10.1515/ijsl-2020-2119 [Google Scholar] [Crossref]

29. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

30. Park, M., & Peck, J. (2022). Beneficial effect of multilingualism in resolving L3 structural complexity: Attention shift from familiar cue to critical cue. Linguistic Research, 39(Special Issue), 1–27. https://doi.org/10.17250/khisli.39..202209.001 [Google Scholar] [Crossref]

31. Peng, C. F. (2025). Factors influencing Malay language learning among Malaysian Chinese independent secondary school students. Jurnal Arbitrer, 12(3), 304–321. https://doi.org/10.25077/ar.12.3.304-321.2025 [Google Scholar] [Crossref]

32. Rao, A., Thi-Nga, H., & Siong, C. E. (2022). Punctuation restoration for Singaporean spoken languages: English, Malay, and Mandarin. In Proceedings of the 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 546–552). [Google Scholar] [Crossref]

33. Shaharuddin, N., Ujang, I. R. M., Maruan, K., Zulkifli, N. W., & Awang, S. (2026). Barriers to health care utilisation and coping strategies during the COVID-19 pandemic: A cross-sectional survey among the public in Malaysia. Journal of Public Health and Development, 24(2), 290–308. https://doi.org/10.55131/jphd/2026/240220 [Google Scholar] [Crossref]

34. Singh, D., Anand, A., Chaturvedi, A., & Baliyan, N. (2024). IWSLT 2024 Indic track system description paper: Speech-to-text translation from English to multiple low-resource Indian languages. In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024) (pp. 359–364). [Google Scholar] [Crossref]

35. Sujaini, H., Cahyawijaya, S., & Putra, A. B. (2023). Analysis of language model role in improving machine translation accuracy for extremely low-resource languages. Journal of Advances in Information Technology, 14(5), 1073–1081. https://doi.org/10.12720/jait.14.5.1073-1081 [Google Scholar] [Crossref]

36. Tham Wei Hui, D., Jalalian Daghigh, A., & Niu, Y. (2026). A reception study on the impact of visual elements and translation strategies on viewers’ comprehension of culture-specific items in Malay-subtitled Chinese movies. FORUM, 24(1), 109–137. https://doi.org/10.1075/forum.25025.tha [Google Scholar] [Crossref]

37. Vaingankar, J. A., Abdin, E., Chong, S. A., Sambasivam, R., Shafie, S., Ong, H. L., Jeyagurunathan, A., Lau, Y. W., Samari, E., Chua, Y. C., Pang, S., & Subramaniam, M. (2021). Development of the Chinese, Malay and Tamil translations of the Positive Mental Health Instrument: Cross-cultural adaptation, validity and internal consistency. Transcultural Psychiatry, 58(1), 76–95. https://doi.org/10.1177/1363461520976045 [Google Scholar] [Crossref]

38. Vermeer, H. J. (1989). Skopos and commission in translational action. In A. Chesterman (Ed.), Readings in translation theory (pp. 173-187). Oy Finn Lectura Ab. [Google Scholar] [Crossref]

39. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press. [Google Scholar] [Crossref]

40. Wongso, W., Joyoadikusumo, A., Buana, B. S., & Suhartono, D. (2023). Many-to-many multilingual translation model for languages of Indonesia. IEEE Access, 11, 91385–91397. https://doi.org/10.1109/ACCESS.2023.3308818 [Google Scholar] [Crossref]

41. Zheng, F., Marrese-Taylor, E., & Matsuo, Y. (2024). Improving low-resource machine translation for Formosan languages using bilingual lexical resources. In Findings of the Association for Computational Linguistics: ACL 2024 (pp. 11248–11259). [Google Scholar] [Crossref]

42. Zhu, H., & Ang, L. H. (2025a). Confusion or fusion? Cultural diversity in the cosmopolitan translation of Chinese street food names in Malaysia. International Journal of Multilingualism, 22(4), 2697–2718. https://doi.org/10.1080/14790718.2025.2480805 [Google Scholar] [Crossref]

43. Zhu, H., & Ang, L. H. (2025b). Is culture reflected in transliteration? Transliteration of Chinese street food names in Malaysia. SAGE Open, 15(3), 21582440251367256. https://doi.org/10.1177/21582440251367256 [Google Scholar] [Crossref]

• Evaluating the Impacts of Mind Mapping Strategy on Developing EFL Students’ Critical Reading Skills
• Significance of Reading Instructions for Language Improvement in Children with Down Syndrome
• Prenasalised Consonants in Liangmai
• Metadiscourse Matters: Definitions, Models, and Advantages for ESL/ EFL Writing
• Blank Minds and Stuck Voices: Understanding and Addressing Cognitive Anxiety in High-Stakes ESL Speaking Tests