  Text style transfer (TST) involves altering the linguistic style of a text
while preserving its core content. This paper focuses on sentiment transfer, a
vital TST subtask (Mukherjee et al., 2022a), across a spectrum of Indian
languages: Hindi, Magahi, Malayalam, Marathi, Punjabi, Odia, Telugu, and Urdu,
expanding upon previous work on English-Bangla sentiment transfer (Mukherjee et
al., 2023). We introduce dedicated datasets of 1,000 positive and 1,000
negative style-parallel sentences for each of these eight languages. We then
evaluate the performance of various benchmark models categorized into parallel,
non-parallel, cross-lingual, and shared learning approaches, including the
Llama2 and GPT-3.5 large language models (LLMs). Our experiments highlight the
significance of parallel data in TST and demonstrate the effectiveness of the
Masked Style Filling (MSF) approach (Mukherjee et al., 2023) in non-parallel
techniques. Moreover, cross-lingual and joint multilingual learning methods
show promise, offering insights into selecting optimal models tailored to the
specific language and task requirements. To the best of our knowledge, this
work represents the first comprehensive exploration of the TST task as
sentiment transfer across a diverse set of languages.
