The Challenge in Multilingual NLP
The increasing availability of digital text in diverse languages and scripts presents a significant challenge for natural language processing (NLP). Multilingual pre-trained language models (mPLMs) often struggle to handle transliterated data effectively, leading to performance degradation.
Current Limitations
Models like XLM-R and Glot500 perform well with text in their original scripts but struggle significantly with transliterated text due to ambiguities and tokenization issues. These limitations degrade their performance in cross-lingual tasks, making them less effective when handling text converted into a common script such as Latin.
Introducing TRANSMI Framework
TRANSMI is a framework designed to enhance mPLMs for transliterated data without requiring additional training. It modifies existing mPLMs using three merge modes—Min-Merge, Average-Merge, and Max-Merge—to incorporate transliterated subwords into their vocabularies, thereby addressing transliteration ambiguities and improving cross-lingual task performance.
Performance and Results
TRANSMI-modified models outperform their original versions in various tasks like sentence retrieval, text classification, and sequence labeling. This modification ensures that models retain their original capabilities while adapting to the nuances of transliterated text, thus enhancing their overall performance in multilingual NLP applications.
Impact and Future Potential
TRANSMI offers a practical and innovative solution to a complex problem, providing a strong foundation for further advancements in multilingual NLP and improving global communication and information processing.
Evolving with AI Solutions
If you want to evolve your company with AI, stay competitive, and use TRANSMI to create baseline models adapted for transliterated data from existing mPLMs. Discover how AI can redefine your way of work, identify automation opportunities, define KPIs, select an AI solution, and implement gradually.
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