Hi-SEMFLOW: Lie Algebra–Based Semantic Flow for Span-Level Informal Language Identification in Hindi
Year: 2026
Type: conference-paper
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, 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-4.9kbenchmark 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.
Institution: Indian Institute of Technology Mandi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166548218