MTML 1.0: A Novel Interlingua Knowledge Representation Model for Machine Translation

Machine translation is one of the major areas of both computational linguistics and artificial intelligence that employs computer algorithms to automatically translate text between different natural languages. At present, the advent of Large Language Models (LLMs) has revolutionized this field, marking a significant turning point in its evolution. Despite their impressive capabilities, LLMs still fall short of achieving human-like translation due to key limitations, namely lack of transparency, explainability, and interpretability, the production of non-deterministic outputs, and insufficient support for low-resource languages. To address these challenges, incorporating human-aided translation mechanisms that reflect how the human brain performs translation is effective. Therefore, from a computer science perspective, this motivates the development of a novel hybrid machine translation approach that integrates a rule-based approach with LLM-based methods. This study presents a novel rule-based interlingual knowledge representation model named MTML 1.0 that has been designed and implemented to accurately analyze source language input and systematically structure the resulting linguistic information to facilitate applications, including target language generation and question-answering systems. The MTML 1.0 system consists of four key modules, namely the preprocessing module, morphological analyzer module, syntax analyzer module, and semantic analyzer module. Furthermore, the system has been fully implemented as a web-based application using the Python programming language, with spaCy serving as the foundation for natural language processing tasks. Finally, the functionality of the system has been demonstrated through the development of a prototype question-answering system.

@article{Goonatilleke2025,
  title     = {MTML 1.0: A Novel Interlingua Knowledge Representation Model for Machine Translation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {M. A. S. T Goonatilleke and B Hettige and A. M. R. R Bandara},
  doi       = {10.14569/IJACSA.2025.0161243},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161243}
}