The study investigates whether morphological correctness influences tokenization effectiveness in Tagalog, a language with infix morphology that disrupts contiguous roots. Researchers developed MorphAustro‑Tok, a tokenizer that preserves the root as a contiguous unit and separates infixes, and compared it to a standard BPE baseline and a scrambled‑morpheme control that randomizes infix placement. Across multiple runs, MorphAustro‑Tok consistently outperformed the baseline, but the scrambled control showed no significant difference, indicating that morphological correctness does not contribute to the observed gains at this scale.
arXiv:2606. 15144v1 Announce Type: cross Abstract: Large language models (LLMs) process text as sequences of subword tokens, which can obscure the character-level and morphological structure that underlies word formation.
MoVoC introduces a morphology-aware subword construction method for Ge'ez script languages, integrating supervised morphological analysis with Byte Pair Encoding to preserve morphological boundaries while retaining lexical meaning. The resulting MoVoC-Tok tokenizer outperforms baselines in MorphScore and morpheme boundary precision across several low-resource, morphologically complex languages, notably achieving significant gains on Tigre through cross-lingual application. The authors also release morpheme-annotated datasets and a morpheme-aware vocabulary for public use.
arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.
The paper shows that HuggingFace’s ByteLevel pre‑tokenizer, which treats a word as a sequence of Unicode letters, splits abugida scripts at every vowel sign, creating a training‑free lower bound on tokenizer fertility. Across 26 languages, all 17 abugidas exhibit increased token counts (up to 9×), while Latin, Cyrillic, Hangul, and Han remain unchanged. The authors demonstrate that correcting the character class reduces Nepali token counts, improves model performance, and that this issue is widespread in popular HuggingFace models.
The paper investigates Japanese past‑tense verb inflection in neural morphological generation systems, focusing on a rare irregular subtype where stems end in /e/ and require gemination before the suffix. Despite overall high accuracy (>97%), this <1% subclass accounts for 30–43% of residual errors, amplifying its impact by 34–48 times its frequency. Ablation experiments show that removing this specific subtype yields larger accuracy gains than removing all irregular verbs, highlighting the role of low‑frequency patterns and orthographic processes in error concentration.
The article examines how Byte‑Pair Encoding (BPE) tokenization handles Polish, an inflectional language, and finds that BPE tends to stabilize frequent surface fragments of grammatical exponents rather than true grammatical categories. It introduces the concept of grammatical form anchoring, showing that certain Polish verb forms can signal the speaking subject without an explicit pronoun, and highlights that language models may lack a stable grammatical "I" and can shift gender or mirror user forms. The study proposes Roclawski’s segmentation‑flexional forms as a diagnostic framework and suggests that more stable Polish modeling would require sublexical stabilization, anchoring grammatical form in the inflectional system, and maintaining the grammatical "I" in dialogue.
The study evaluates phonologically informed tokenization for German speech recognition using a wav2vec 2.0 backbone fine‑tuned with CTC. Three tokenizer families—pretrained multilingual characters, data‑driven BPE, and phonologically informed units from syllabification and grapheme‑to‑phoneme conversion—are compared across 40 fine‑tunes on three German test sets (in‑domain read, dialectal spontaneous, and standard spontaneous speech). Results show that phonologically informed tokenizers match BPE and character baselines in‑domain, but under domain shift their performance depends on vocabulary size rather than linguistic variation, with syllable‑aware tokenization benefiting dialectal and spontaneous speech at small vocabularies; a phoneme‑level confusion analysis indicates that the acoustic encoder dominates error patterns.
arXiv:2603.03510v3 Announce Type: replace-cross Abstract: This study investigates the diverse characteristics of nouns, focusing on both semantic (e.g., countable/uncountable) and morphosyntactic (e....
arXiv:2606. 18717v1 Announce Type: cross 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.
The paper introduces the Latent Core Tokenizer (LCT), a language‑agnostic method that first discovers reusable linguistic units using Minimum Description Length, entropy‑based boundary signals, and morphotactic constraints before building a shared vocabulary. With a 200K‑token vocabulary across 104 languages, LCT shows lower fertility and higher MorphScore than BPE, Unigram, and parity‑aware BPE, while keeping tokenization cost comparable across languages. On four multilingual downstream benchmarks, LCT outperforms the baselines by 1.48, 1.83, and 2.00 aggregate points, demonstrating that compression alone does not guarantee representation quality and underscoring the role of morphology‑driven structural discovery.
SuTRA (Structurally-Unified Tokenization with Root Awareness) is a morphology-aware tokenization algorithm designed to address the problem of Morphological Shattering in morphologically rich Indic languages. It preserves the indivisibility of aksharas—complex orthographic syllables—by penalizing merges that cross morphological boundaries, thereby reducing over-fragmentation of words. The authors also release a new morphological segmentation dataset for Hindi, Marathi, and Gujarati, and demonstrate that SuTRA improves morphological alignment by up to 14.7% and semantic recoverability by 34% over BPE, leading to an average machine translation gain of +8.08 chrF2.
The paper introduces MoirfEolas, a dataset of over 35,000 Irish words annotated with their morphological components, and CríochScore, a metric that measures how well tokenization aligns with these morphological boundaries. Using CríochScore, the authors evaluate common tokenization algorithms and find that the Unigram Language Model best aligns with Irish morphology. They also discuss trade‑offs between morphological alignment, compression, and vocabulary efficiency, offering practical guidance for Irish NLP development.