Symphonym: Universal Phonetic Embeddings for Cross-Script Toponym Matching

Abstract

Matching place names across writing systems is a persistent obstacle to integrating multilingual geographic sources, from modern gazetteers to medieval itineraries and colonial-era surveys. Existing approaches rely on language-specific phonetic algorithms or on romanisation that discards phonetic information, and none generalises across scripts. Symphonym maps toponyms from thirty-six writing systems into a unified 128-dimensional phonetic space, enabling direct cross-script comparison without language identification or phonetic resources at inference time. A Teacher-Student distillation architecture learns from articulatory features of IPA transcriptions and transfers this knowledge to a character-level Student. Trained on 73.5 million toponyms from GeoNames, Wikidata and the Getty TGN, the Student achieves the highest Recall@1 (89.3%) and MRR (92.8%) on the MEHDIE benchmark of medieval Hebrew and Arabic toponym matches, which is independent of the training data. An ablation on raw articulatory features alone reaches only 45.0% MRR. This revision reports a second model generation and three results that qualify the first: a corpus defect caused the original system to learn Chinese characters with Japanese readings, which we correct and quantify; the encoder weighted character content far above order, admitting 70.5% of random anagrams past the retrieval gate, which targeted negatives reduce to 5.0% without loss of typo tolerance; and better retrieval came with slightly worse separation of true from false matches. We report where the method wins decisively (across scripts) and where string metrics remain preferable (within the Latin script), and document an independent out-of-domain deployment on archival personal names.