SuTRA : Structurally-Unified Tokenization with Root Awareness
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Computer Science > Computation and Language
Title:SuTRA : Structurally-Unified Tokenization with Root Awareness
Abstract:Existing subword tokenizers optimize statistical compression but ignore morphological structure, particularly the relationship between roots and affixes. This is harmful for morphologically rich Indic languages, where basic units are complex orthographic syllables (aksharas) rather than letters. Frequency-based methods over-fragment words, arbitrarily splitting roots and affixes - a phenomenon we term Morphological Shattering. We propose SuTRA (Structurally-Unified Tokenization with Root Awareness), a morphology-aware algorithm that preserves akshara indivisibility and penalizes merges crossing morphological boundaries. We also release a new morphological segmentation dataset for Hindi, Marathi, and Gujarati. SuTRA reduces shattering, achieving peak gains of +14.7% in morphological alignment (Boundary F1) and +34% in semantic recoverability (Hindi) over BPE. These structural gains yield an average improvement of +8.08 chrF2 in machine translation.
| Comments: | Accepted at Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.18087 [cs.CL] |
| (or arXiv:2608.18087v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18087
arXiv-issued DOI via DataCite
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