PolyIPA -- Multilingual Phoneme-to-Grapheme Conversion Model
Davor Lauc·December 12, 2024
Summary
PolyIPA is a multilingual phoneme-to-grapheme conversion model designed for tasks like name transliteration, onomastic research, and information retrieval. It leverages techniques such as IPA2vec and similarIPA for data augmentation, achieving a mean Character Error Rate of 0.055 and a character-level BLEU score of 0.914, particularly effective for languages with shallow orthographies. Beam search enhances the model's practical utility, reducing the effective error rate by 52.7% with top-3 candidates. The model's performance is evaluated across various languages, with strong results for those with shallow orthographies, supporting the Orthographic Depth Hypothesis. Future research aims to improve model architecture, data collection, and handling of complex morphophonological rules and tone languages.
Introduction
Background
Overview of phoneme-to-grapheme conversion challenges
Importance of multilingual models in name transliteration, onomastic research, and information retrieval
Objective
Aim of the PolyIPA model development
Key performance indicators (mean Character Error Rate, character-level BLEU score)
Method
Data Collection
Techniques for gathering diverse linguistic data
Importance of multilingual datasets for model training
Data Preprocessing
Methods for cleaning and standardizing input data
Role of data augmentation (IPA2vec, similarIPA) in enhancing model performance
Model Architecture
Core Components
Overview of the model's structure
Integration of phoneme and grapheme representations
Training and Optimization
Training strategies for multilingual datasets
Techniques for improving model generalization
Evaluation
Performance Metrics
Character Error Rate (CER)
Character-level BLEU score
Results Across Languages
Analysis of model performance on languages with shallow orthographies
Support for the Orthographic Depth Hypothesis
Practical Applications
Enhancements with Beam Search
Implementation of beam search for improved candidate selection
Reduction in effective error rate by 52.7%
Future Directions
Research priorities for model improvement
Challenges in handling complex morphophonological rules and tone languages
Conclusion
Summary of PolyIPA's Contributions
Implications for Future Research and Development
Outlook on Multilingual Language Processing
Basic info
papers
computation and language
artificial intelligence
Advanced features