mlsanityData to 7 Oct 2026

PolyIPA -- Multilingual Phoneme-to-Grapheme Conversion Model

arXiv 2412.09102 ↗First posted 12 Dec 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

This paper presents PolyIPA, a novel multilingual phoneme-to-grapheme conversion model designed for multilingual name transliteration, onomastic research, and information retrieval. The model leverages two helper models developed for data augmentation: IPA2vec for finding soundalikes across languages, and similarIPA for handling phonetic notation variations. Evaluated on a test set that spans multiple languages and writing systems, the model achieves a mean Character Error Rate of 0.055 and a character-level BLEU score of 0.914, with particularly strong performance on languages with shallow orthographies. The implementation of beam search further improves practical utility, with top-3 candidates reducing the effective error rate by 52.7% (to CER: 0.026), demonstrating the model's effectiveness for cross-linguistic applications.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 46 references that are in the map. First posted on arXiv 12 Dec 2024.

Builds on

9 of 46 references.

References · 9

  1. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  2. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  3. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  4. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 738
  5. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 504
  6. 2022ByT5 model for massively multilingual grapheme-to-phoneme conversioncited by 49
  7. 2024LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Studycited by 9
  8. 2020Character-level Transformer-based Neural Machine Translationcited by 7
  9. 2023PWESuite: Phonetic Word Embeddings and Tasks They Facilitatecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025POWSM: A Phonetic Open Whisper-Style Speech Foundation Modelcited by 14
  2. 2026Advancing LLM-based phoneme-to-grapheme for multilingual speech recognitioncited by 0
  3. 2026Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinesecited by 0
  4. 2026One Form to Transfer Them All: Pretraining Multilingual Language Models Beyond Native Orthographycited by 0