Summary of Polyipa — Multilingual Phoneme-to-grapheme Conversion Model, by Davor Lauc
PolyIPA – Multilingual Phoneme-to-Grapheme Conversion Model
by Davor Lauc
First submitted to arxiv on: 12 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 PolyIPA is a machine learning model designed to convert phonemes into written characters across multiple languages and writing systems. The model uses two helper models, IPA2vec and similarIPA, to handle variations in soundalike and phonetic notation data augmentation. Evaluated on a test set spanning different languages and writing systems, PolyIPA achieves a mean Character Error Rate of 0.055 and a character-level BLEU score of 0.914, with strong performance on languages with shallow orthographies. The implementation of beam search further improves practical utility, reducing the effective error rate by 52.7%. This model demonstrates effectiveness for cross-linguistic applications in multilingual name transliteration, onomastic research, and information retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PolyIPA is a special kind of computer program that helps translate sounds into written words across different languages. It uses some extra tools to make sure it works well with different writing systems. The program was tested on many languages and showed good results, especially when dealing with simpler writing systems. It even got better after adding an extra feature called beam search. This program is useful for things like translating names from one language to another, researching sounds in languages, and finding information. |
Keywords
» Artificial intelligence » Bleu » Data augmentation » Machine learning
