Summary of Ayutthayaalpha: a Thai-latin Script Transliteration Transformer, by Davor Lauc et al.
AyutthayaAlpha: A Thai-Latin Script Transliteration Transformer
by Davor Lauc, Attapol Rutherford, Weerin Wongwarawipatr
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study introduces AyutthayaAlpha, a transformer-based machine learning model designed for transliterating Thai proper names into Latin script. The system achieves state-of-the-art performance with 82.32% first-token accuracy and 95.24% first-three-token accuracy, while maintaining a low character error rate of 0.0047. AyutthayaAlpha uses a novel two-model approach, combining linguistic rules with deep learning. The model is trained on a curated dataset of 1.2 million Thai-Latin name pairs, augmented to 2.7 million examples. Evaluations against existing transliteration methods and human expert benchmarks demonstrate the system’s superior accuracy in capturing personal and cultural preferences in name romanization. AyutthayaAlpha has practical applications in cross-lingual information retrieval, international data standardization, and identity verification systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new machine learning model called AyutthayaAlpha that helps translate Thai names into Latin letters. The model is very good at getting the first part of the name right (82.32%) and the first three parts right (95.24%). It also makes very few mistakes in writing out the characters. To make this happen, the model uses a special way of combining rules about language with deep learning. It was trained on 1.2 million pairs of Thai-Latin names, which is then expanded to 2.7 million examples. The results show that this model does a better job than others at translating names in a way that respects cultural and personal preferences. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Token » Transformer
