Leveraging Low-resource Parallel Data for Text Style Transfer
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10476023" target="_blank" >RIV/00216208:11320/23:10476023 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.inlg-main.27" target="_blank" >https://aclanthology.org/2023.inlg-main.27</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Leveraging Low-resource Parallel Data for Text Style Transfer
Original language description
Text style transfer (TST) involves transforming a text into a desired style while approximately preserving its content. The biggest challenge in TST in the general lack of parallel data. Many existing approaches rely on complex models using substantial non-parallel data, with mixed results. In this paper, we leverage a pretrained BART language model with minimal parallel data and incorporate low-resource methods such as hyperparameter tuning, data augmentation, and self-training, which have not been explored in TST. We further include novel style-based rewards in the training loss. Through extensive experiments in sentiment transfer, a sub-task of TST, we demonstrate that our simple yet effective approaches achieve well-balanced results, surpassing non-parallel approaches and highlighting the usefulness of parallel data even in small amounts.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
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 16th International Natural Language Generation Conference
ISBN
979-8-89176-001-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
388-395
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Praha, Czechia
Event date
Sep 13, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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