@inproceedings{sarker-etal-2026-still,
title = "Still Loading@{D}ravidian{L}ang{T}ech 2026: {T}elugu Prompt-Style Recovery using Multilingual Transformers",
author = "Sarker, Samonwita and
Sami, Isnat Mehrin and
Mojumder, Priyontee and
Mallik, Arpita and
Murad, Hasan",
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://aclanthology.org/2026.dravidianlangtech-1.58/",
pages = "371--375",
ISBN = "979-8-89176-401-9",
abstract = "This paper describes the system that our Still-Loading team designed to run the Telugu Prompt-Style Recovery shared task at DravidianLangTech@ACL 2026. The purpose of the given task is categorizing Telugu transcript passages as belonging to one of 9 communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We compared several multilingual Transformer-based models, i.e. MuRIL, XLM-RoBERTa-Large, mBERT, and IndicBERTv2. We chose a ``Turbo Sandwich'' preprocessing strategy which helps to give more emphasis to lexical deltas, in addition to Focal Loss. Our system based on the MuRIL was rated at the 7th place in the official leaderboard with a Macro-F1 rating of 0.1703. The source code to reproduce our experiments is publicly available on Still-Loading-Prompt-Recovery-for-LLM-in-Telugu (https://github.com/Priyontee1713/Still-Loading-Prompt-Recovery-for-LLM-in-Telugu)."
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<abstract>This paper describes the system that our Still-Loading team designed to run the Telugu Prompt-Style Recovery shared task at DravidianLangTech@ACL 2026. The purpose of the given task is categorizing Telugu transcript passages as belonging to one of 9 communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We compared several multilingual Transformer-based models, i.e. MuRIL, XLM-RoBERTa-Large, mBERT, and IndicBERTv2. We chose a “Turbo Sandwich” preprocessing strategy which helps to give more emphasis to lexical deltas, in addition to Focal Loss. Our system based on the MuRIL was rated at the 7th place in the official leaderboard with a Macro-F1 rating of 0.1703. The source code to reproduce our experiments is publicly available on Still-Loading-Prompt-Recovery-for-LLM-in-Telugu (https://github.com/Priyontee1713/Still-Loading-Prompt-Recovery-for-LLM-in-Telugu).</abstract>
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%0 Conference Proceedings
%T Still Loading@DravidianLangTech 2026: Telugu Prompt-Style Recovery using Multilingual Transformers
%A Sarker, Samonwita
%A Sami, Isnat Mehrin
%A Mojumder, Priyontee
%A Mallik, Arpita
%A Murad, Hasan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F sarker-etal-2026-still
%X This paper describes the system that our Still-Loading team designed to run the Telugu Prompt-Style Recovery shared task at DravidianLangTech@ACL 2026. The purpose of the given task is categorizing Telugu transcript passages as belonging to one of 9 communicative styles: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive. We compared several multilingual Transformer-based models, i.e. MuRIL, XLM-RoBERTa-Large, mBERT, and IndicBERTv2. We chose a “Turbo Sandwich” preprocessing strategy which helps to give more emphasis to lexical deltas, in addition to Focal Loss. Our system based on the MuRIL was rated at the 7th place in the official leaderboard with a Macro-F1 rating of 0.1703. The source code to reproduce our experiments is publicly available on Still-Loading-Prompt-Recovery-for-LLM-in-Telugu (https://github.com/Priyontee1713/Still-Loading-Prompt-Recovery-for-LLM-in-Telugu).
%U https://aclanthology.org/2026.dravidianlangtech-1.58/
%P 371-375
Markdown (Informal)
[Still Loading@DravidianLangTech 2026: Telugu Prompt-Style Recovery using Multilingual Transformers](https://aclanthology.org/2026.dravidianlangtech-1.58/) (Sarker et al., DravidianLangTech 2026)
ACL