Text Detoxification as Style Transfer in English and Hindi

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10492594" target="_blank" >RIV/00216208:11320/23:10492594 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2023.icon-1.13" target="_blank" >https://aclanthology.org/2023.icon-1.13</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Text Detoxification as Style Transfer in English and Hindi

Original language description

This paper focuses on text detoxification, i.e., automatically converting toxic text into nontoxic text. This task contributes to safer and more respectful online communication and can be considered a Text Style Transfer (TST) task, where the text’s style changes while its content is preserved. We present three approaches: (i) knowledge transfer from a similar task (ii) multi-task learning approach, combining sequence-to-sequence modeling with various toxicity classification tasks, and (iii) delete and reconstruct approach. To support our research, we utilize a dataset provided by Dementieva et al. (2021), which contains multiple versions of detoxified texts corresponding to toxic texts. In our experiments, we selected the best variants through expert human annotators, creating a dataset where each toxic sentence is paired with a single, appropriate detoxified version. Additionally, we introduced a small Hindi parallel dataset, aligning with a part of the English dataset, suitable for evaluation purpo

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů