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A Conceptual Framework for Malay-English Code-Switched Neural Machine Translation

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Advances in Intelligent Computing Techniques and Applications (IRICT 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 211))

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Abstract

This paper presents a conceptual framework for addressing the challenges in translating Malay-English code-switched texts using neural machine translation. The framework comprises of three phases: language identification, code-switching type identification, and segment translation. In the language identification phase, a trained model attaches language tags (M for Malay, E for English, M-E for ambiguous) to each word. Code-switching types, including intra-sentential (AM and AE) and inter-sentential (EM and EE), are identified in the code-switching type identification phase. The segment translation phase utilizes an RNN model trained on a Malay-English code-switched parallel corpus and a homonyms dictionary with POS tagging. Our framework addresses linguistic characteristics, informal language usage, structural differences, and ambiguity. It contributes to the advancement of machine translation in code-switching contexts. Despite the conceptual nature of the framework without concrete results, our thorough analysis of code-switching types and associated challenges lays a foundation for future model enhancements. By providing a comprehensive solution, it enables more accurate and effective communication in code-switched language scenarios. Further research can build upon this framework to enhance code-switched translation models.

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Acknowledgement

This research was supported by the Ministry of Higher Education (MOHE) through the Fundamental Research Grant Scheme (FRGS/1/2021/ICT02/UTM/02).

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Yit Khee Wong & Sharin Hazlin Huspi

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  1. Yit Khee Wong
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  2. Sharin Hazlin Huspi
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Correspondence to Yit Khee Wong .

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Editors and Affiliations

  1. School of Computing and Digital Techn., Birmingham City University, BIRMINGHAM, UK

    Faisal Saeed

  2. Department of Business Analytics, Sunway Business School, Sunway University, Selangor, Malaysia

    Fathey Mohammed

  3. Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV, USA

    Yousef Fazea

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Wong, Y.K., Huspi, S.H. (2024). A Conceptual Framework for Malay-English Code-Switched Neural Machine Translation. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_5

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