Multilingual Text Style Transfer: Datasets & Models for Indian Languages

• URL: http://arxiv.org/abs/2405.20805v3
• Date: Tue, 27 Aug 2024 06:51:00 GMT
• Title: Multilingual Text Style Transfer: Datasets & Models for Indian Languages
• Authors: Sourabrata Mukherjee, Atul Kr. Ojha, Akanksha Bansal, Deepak Alok, John P. McCrae, Ondřej Dušek,
• Abstract summary: This paper focuses on sentiment transfer, a popular TST subtask, across a spectrum of Indian languages. We introduce dedicated datasets of 1,000 positive and 1,000 negative style-parallel sentences for each of these eight languages. We evaluate the performance of various benchmark models categorized into parallel, non-parallel, cross-lingual, and shared learning approaches.
• Score: 1.116636487692753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text style transfer (TST) involves altering the linguistic style of a text while preserving its core content. This paper focuses on sentiment transfer, a popular TST subtask, 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.

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