@inproceedings{chandrasekar-etal-2026-medhastra,
    title = "{M}ed{H}astra@{D}ravidian{L}ang{T}ech 2026: Piecewise Style Classification for {T}elugu Prompt Recovery Using {XLM}-{R}o{BERT}a",
    author = "Chandrasekar, Shruti  and
      S, Vedajanaani R  and
      P, Vijayalakshmi",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.46/",
    pages = "301--305",
    ISBN = "979-8-89176-401-9",
    abstract = "We present a system for the DravidianLangTech @ ACL 2026 shared task on TeluguPrompt-Style Recovery(B et al., 2026). The task requires classifying Telugu text into one of nine communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative and Persuasive. Our approach fine-tunes the multilingual XLMRoBERTa base model with a piecewise segment comparison strategy that evaluates distinct stylistic markers across sentence segments,enabling richer contextual discrimination between visually similar styles. Evaluated on the official test set, our system achieves a Macro F1score of 0.1205, Accuracy of 0.1196, Precision of 0.1205 and Recall of 0.1231. We analyze the challenges of stylistic ambiguity in low resource Telugu NLP and discuss directions for future improvement."
}