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.

翻译：文本风格迁移（TST）旨在改变文本的语言风格，同时保留其核心内容。本文聚焦于情感迁移这一关键的TST子任务（Mukherjee et al., 2022a），并将其扩展至一系列印度语言：印地语、马加希语、马拉雅拉姆语、马拉地语、旁遮普语、奥里亚语、泰卢固语和乌尔都语，此研究基于先前在英语-孟加拉语情感迁移方面的工作（Mukherjee et al., 2023）。我们为这八种语言分别构建了专用数据集，每种语言包含1,000条积极风格和1,000条消极风格的平行句对。随后，我们评估了多种基准模型的性能，这些模型分为平行、非平行、跨语言和共享学习等不同方法，其中包括Llama2和GPT-3.5等大语言模型（LLM）。实验结果表明，平行数据在TST任务中至关重要，并验证了掩码风格填充（MSF）方法（Mukherjee et al., 2023）在非平行技术中的有效性。此外，跨语言和联合多语言学习方法展现出潜力，为针对特定语言和任务需求选择最优模型提供了见解。据我们所知，本研究首次对TST任务（即情感迁移）在多种语言集合上进行了全面探索。