Multilingual Text Style Transfer: Datasets & Models for Indian Languages
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492886" target="_blank" >RIV/00216208:11320/24:10492886 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.inlg-main.41" target="_blank" >https://aclanthology.org/2024.inlg-main.41</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Multilingual Text Style Transfer: Datasets & Models for Indian Languages
Original language description
Text style transfer (TST) involves altering the linguistic style of a text while preserving its style-independent 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. 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 in non-parallel techniques. Moreover, cross-lingual and joint multilingual learning methods show promise, offering insights into selecting optimal models tailored to the
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Natural Language Generation Conference
ISBN
979-8-89176-122-3
ISSN
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e-ISSN
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Number of pages
29
Pages from-to
494-522
Publisher name
Association for Computational Linguistics
Place of publication
Kerrville, TX, USA
Event location
Tokyo, Japan
Event date
Sep 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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