We present, to our knowledge, the first systematic evaluation of tokenization quality for informal Hindi expressions, combining static, downstream, and robustness analyses. Our investigation centers on three questions: (RQ1) how well tokenizers preserve informal expression units using static boundary and integrity metrics, (RQ2) how tokenization choices affect downstream identification of informal expressions, and (RQ3) how robust tokenizers remain under orthographic variation, romanization, and noisy spelling. Across multilingual, Indic-focused, and byte-level tokenizers, we find that Indic-oriented models (e.g., MuRIL, IndicBERT) preserve expression boundaries better and achieve higher downstream F1 on clean text than generic multilingual models (e.g., mBERT, XLM-R). However, all tokenizers exhibit severe degradation under romanization, with phrase integrity rates approaching zero. These findings demonstrate that tokenization constitutes a hidden but critical bottleneck for informal Hindi NLP, particularly in cross-script settings, and motivate the need for tokenization strategies that explicitly account for phrase-level semantics and orthographic variation.

Informal Hindi text frequently contains multi-token slang and idiomatic expressions whose correct identification requires consistent span boundaries. Transformer-based token classifiers, despite strong contextual representations, often produce fragmented or structurally invalid BIO sequences due to largely local predictions. We propose Hi-SEMFLOW, a Lie algebra–based semantic flow framework that models span consistency as a continuous refinement process over label logits. Instead of discrete structured decoding (e.g., CRFs), Hi-SEMFLOW learns context-dependent transition operators derived from antisymmetric generators and propagates structural information through smooth, fully differentiable transformations. This formulation integrates structural bias directly into end-to-end training without requiring dynamic programming or hard decoding constraints. Experiments on the HiSlang-4.9k benchmark show that Hi-SEMFLOW improves span-level F1 by up to 2–3 absolute points and yields consistent macro-F1 gains across Hindi-pretrained encoders. Extensive ablations demonstrate that continuous geometric refinement provides a flexible and effective alternative to discrete structured decoding for span-centric sequence labeling.