@inproceedings{ravikiran-etal-2026-hi,
    title = "Hi-{SEMFLOW}: Lie Algebra{--}Based Semantic Flow for Span-Level Informal Language Identification in {H}indi",
    author = "Ravikiran, Manikandan  and
      Tiwari, Tanmay  and
      Gupta, Vibhu  and
      Saluja, Rohit",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini",
    booktitle = "Proceedings of the Second workshop on Challenges in Processing {S}outh {A}sian Languages ({CH}i{PSAL}2026)",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-workflow/2026.chipsal-1.14/",
    doi = "10.63317/2duxp4m72ddj",
    pages = "137--153",
    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."
}