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.
Text-to-speech (TTS) for Modern Hebrew is challenged by the language's orthographic complexity, with existing solutions ignoring underspecified phonetic features such as stress. We present a framework for more phonetically accurate Hebrew TTS with four contributions: (1) Phonikud, an open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified International Phonetic Alphabet (IPA) transcriptions, designed by augmenting a base diacritizer. (2) The ILSpeech corpus of paired Hebrew audio, text, and expert IPA annotations. (3) A benchmark for the previously unmeasured task of Hebrew G2P conversion. (4) Hebrew audio-to-IPA models capturing previously disregarded phonetic details for automatic TTS evaluation. Our results show that Phonikud more accurately predicts Hebrew phonemes than prior methods, and that small, local TTS models with phonetic input from Phonikud approach large proprietary systems. We release our code, data, and models at https://phonikud.github.io.
Yakov Kolani, Maxim Melichov, Cobi Calev +1
Independent Researcher · Reichman University, Israel · Cisco Systems +2
Closely related languages written in different scripts expose little surface overlap to multilingual models, limiting cross-lingual transfer. We compare two approaches to script unification: the general-purpose uroman romanizer and the family-specific Common Turkic Script (CTS). We train matched fastText models on transliterated Wikipedia corpora from 11 Turkic languages and evaluate them on WikiANN named entity recognition and Universal Dependencies part-of-speech tagging. CTS and uroman show no significant difference on NER, while both substantially outperform the official monolingual fastText baselines. POS results reveal no universal winner: language-specific differences are associated with the cross-lingual character n-gram coverage induced by each representation, while within-language coverage becomes more important when target-language supervision is available. Although CANINE-c achieves higher overall POS averages, the substantially simpler fastText-based systems remain competitive on several treebanks. Overall, the effectiveness of script unification depends on the language, the induced subword overlap, and the available supervision.
Multilingual language models transfer knowledge across languages through shared subword vocabulary, a mechanism that breaks down when related languages use different writing systems. Prior work addresses this via script equalization (romanization or IPA transcription), but direct comparisons are rare; the focus has been on encoder-only models, with most work adapting existing pretrained models. We systematically compare different input representations in autoregressive multilingual pretraining, comparing orthographic text, IPA, and romanization in a controlled setup across three scales (467M, 709M, and 1.03B) on eight languages in four typologically motivated pairs. Across a wide range of downstream tasks on seen and unseen languages, romanized pretraining yields the strongest cross-lingual transfer, and the advantage over text widens with scale. IPA improves over text in most settings but trails romanization. Surprisingly, finetuning a text-pretrained model on romanized data hurts performance on languages already covered by the base model, only marginally helping when the model lacks script coverage. Our results indicate that for multilingual models spanning typologically diverse scripts, to obtain maximum benefits, romanization should be treated as a core design choice applied at pretraining rather than a post hoc fix.
Muge Zhang, Aaron Jencks, Krishna Badikela +2
Department of Computer Science and Engineering, Ohio State University · Paul G. Allen School of Computer Science & Engineering, University of Washington