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Morpheus: A Morphology-Aware Neural Tokenizer and Word Embedder for Turkish

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arXiv:2606. 18717v1 Announce Type: new Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text.

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Authors: Tolga \c{S}akar
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Morpheus: A Morphology-Aware Neural Tokenizer and Word Embedder for Turkish overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, word, alignment, soft, eval, token, emit. Source: arxiv cs.CL (NLP).
arXiv: 2606.18717

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