Despite different countries and social platform regulations, digital abusive speech persists
as a significant challenge. One of the way to tackle abusive, or more specifically, toxic …

From pre-trained language model (PLM) to large language model (LLM), the field of natural
language processing (NLP) has witnessed steep performance gains and wide practical …

Style representation learning builds content-independent representations of author style in
text. Stylometry, the analysis of style in text, is often performed by expert forensic linguists …

Using novel approaches to dataset development, the Biasly dataset captures the nuance
and subtlety of misogyny in ways that are unique within the literature. Built in collaboration …

Text detoxification aims to minimize the risk of language models producing toxic content.
Existing detoxification methods of directly constraining the model output or further training …

Content moderation is the process of flagging content based on pre-defined platform rules.
There has been a growing need for AI moderators to safeguard users as well as protect the …

Offensive and toxic text on social media platforms can lead to polarization and divisiveness
within online communities and hinders constructive dialogue. Text detoxification is a crucial …

Paraphrasing of offensive content is a better alternative to content removal and helps
improve civility in a communication environment. Supervised paraphrasers; however, rely …

Ensuring robust safety measures across a wide range of scenarios is crucial for user-facing
systems. While Large Language Models (LLMs) can generate valuable data for safety …

Most existing stylistic text rewriting methods and evaluation metrics operate on a sentence
level, but ignoring the broader context of the text can lead to preferring generic, ambiguous …