@inproceedings{mukherjee-etal-2023-low,
title = "Low-Resource Text Style Transfer for {B}angla: Data {\&} Models",
author = "Mukherjee, Sourabrata and
Bansal, Akanksha and
Majumdar, Pritha and
Ojha, Atul Kr. and
Du{\v{s}}ek, Ond{\v{r}}ej",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Sadeque, Farig and
Amin, Ruhul",
booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.banglalp-1.5",
doi = "10.18653/v1/2023.banglalp-1.5",
pages = "34--47",
abstract = "Text style transfer (TST) involves modifying the linguistic style of a given text while retaining its core content. This paper addresses the challenging task of text style transfer in the Bangla language, which is low-resourced in this area. We present a novel Bangla dataset that facilitates text sentiment transfer, a subtask of TST, enabling the transformation of positive sentiment sentences to negative and vice versa. To establish a high-quality base for further research, we refined and corrected an existing English dataset of 1,000 sentences for sentiment transfer based on Yelp reviews, and we introduce a new human-translated Bangla dataset that parallels its English counterpart. Furthermore, we offer multiple benchmark models that serve as a validation of the dataset and baseline for further research.",
}
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<abstract>Text style transfer (TST) involves modifying the linguistic style of a given text while retaining its core content. This paper addresses the challenging task of text style transfer in the Bangla language, which is low-resourced in this area. We present a novel Bangla dataset that facilitates text sentiment transfer, a subtask of TST, enabling the transformation of positive sentiment sentences to negative and vice versa. To establish a high-quality base for further research, we refined and corrected an existing English dataset of 1,000 sentences for sentiment transfer based on Yelp reviews, and we introduce a new human-translated Bangla dataset that parallels its English counterpart. Furthermore, we offer multiple benchmark models that serve as a validation of the dataset and baseline for further research.</abstract>
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%0 Conference Proceedings
%T Low-Resource Text Style Transfer for Bangla: Data & Models
%A Mukherjee, Sourabrata
%A Bansal, Akanksha
%A Majumdar, Pritha
%A Ojha, Atul Kr.
%A Dušek, Ondřej
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mukherjee-etal-2023-low
%X Text style transfer (TST) involves modifying the linguistic style of a given text while retaining its core content. This paper addresses the challenging task of text style transfer in the Bangla language, which is low-resourced in this area. We present a novel Bangla dataset that facilitates text sentiment transfer, a subtask of TST, enabling the transformation of positive sentiment sentences to negative and vice versa. To establish a high-quality base for further research, we refined and corrected an existing English dataset of 1,000 sentences for sentiment transfer based on Yelp reviews, and we introduce a new human-translated Bangla dataset that parallels its English counterpart. Furthermore, we offer multiple benchmark models that serve as a validation of the dataset and baseline for further research.
%R 10.18653/v1/2023.banglalp-1.5
%U https://aclanthology.org/2023.banglalp-1.5
%U https://doi.org/10.18653/v1/2023.banglalp-1.5
%P 34-47
Markdown (Informal)
[Low-Resource Text Style Transfer for Bangla: Data & Models](https://aclanthology.org/2023.banglalp-1.5) (Mukherjee et al., BanglaLP 2023)
ACL
- Sourabrata Mukherjee, Akanksha Bansal, Pritha Majumdar, Atul Kr. Ojha, and Ondřej Dušek. 2023. Low-Resource Text Style Transfer for Bangla: Data & Models. In Proceedings of the First Workshop on Bangla Language Processing (BLP-2023), pages 34–47, Singapore. Association for Computational Linguistics.