TRANSMI: A machine learning framework that creates a baseline model adapted to transliteration data from an existing multilingual pre-trained language model mPLM without training.

The increasing availability of digital text in diverse languages ​​and scripts poses significant challenges for natural language processing (NLP). Multilingual pre-trained language models (mPLMs) often struggle to effectively process transliterated data, leading to poor performance. Addressing this issue is important to improve cross-linguistic transfer learning and ensure accurate NLP applications across different languages ​​and scripts, which is essential for global communication and information processing.

Current methods, including models such as XLM-R and Glot500, work well with text in the original script, but become significantly more difficult with transliterated text due to ambiguity and tokenization issues. These limitations reduce performance on cross-linguistic tasks and reduce efficiency when processing text converted to common scripts such as Latin. These models cannot accurately interpret transliterations, which poses a major obstacle to their practicality in multilingual settings.

Researchers at the LMU Munich Center for Information and Language Processing and the Munich Center for Machine Learning (MCML) have introduced TRANSMI, a framework designed to enhance mPLM of transliterated data without the need for additional training. did. TRANSMI modifies existing mPLMs using three merge modes: Min-Merge, Average-Merge, and Max-Merge to address transliteration ambiguity and language Improve performance on inter-task tasks.

TRANSMI integrates new subwords tailored to transliterated data into mPLM's vocabulary, and is especially good in Max-Merge mode for high-resource languages. The framework was tested using a dataset containing transliterated versions of text in scripts such as Cyrillic, Arabic, and Devanagari, and the TRANSMI-modified model was used for sentence retrieval, text classification, and sequencing. It has been shown to perform better than the original version on various tasks such as labeling. This change allows the model to adapt to the nuances of the transliterated text while preserving its original functionality, improving overall performance in multilingual NLP applications.

The dataset used to validate TRANSMI spans a variety of scripts and provides a comprehensive evaluation of its effectiveness. For example, the FURINA model using Max-Merge mode shows a significant improvement in the sequence labeling task, demonstrating the ability of his TRANSMI to process phonograms and reduce problems arising from transliteration ambiguities. It has been. This approach allows mPLM to handle a wider range of languages ​​more accurately, increasing its usefulness in multilingual contexts.

The results show that the TRANSMI modified model achieves higher accuracy compared to the unmodified model. For example, his FURINA model with Max-Merge mode shows significant performance improvements on sequence labeling tasks across different languages ​​and scripts, showing clear improvements in key performance metrics. Masu. These improvements highlight the potential of his TRANSMI as an effective tool to enhance multilingual NLP models, better handle transliterated data, and achieve more accurate interlingual processing. Masu.

In conclusion, TRANSMI addresses the important challenge of improving mPLM performance on transliterated data by modifying existing models without additional training. This framework enhances mPLM's transliteration processing capabilities, leading to significant improvements in cross-linguistic tasks. TRANSMI provides practical and innovative solutions to complex problems and provides a strong foundation for further advances in multilingual NLP and improvements in global communication and information processing.

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