Hi-SEMFLOW: Lie Algebra–Based Semantic Flow for Span-Level Informal Language Identification in Hindi

Abstract

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.

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