Search Results for author:
Found 9 papers, 5 papers with code
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation
We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness.
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation
In both tasks, the system is supposed to generate a text which should be semantically similar to the input text.
MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages
Text detoxification is a textual style transfer (TST) task where a text is paraphrased from a toxic surface form, e. g. featuring rude words, to the neutral register.
Don't lose the message while paraphrasing: A study on content preserving style transfer
Text style transfer techniques are gaining popularity in natural language processing allowing paraphrasing text in the required form: from toxic to neural, from formal to informal, from old to the modern English language, etc.
Error syntax aware augmentation of feedback comment generation dataset
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning.
Studying the role of named entities for content preservation in text style transfer
Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer.
Detecting Text Formality: A Study of Text Classification Approaches
Formality is one of the important characteristics of text documents.
Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language
Toxicity on the Internet, such as hate speech, offenses towards particular users or groups of people, or the use of obscene words, is an acknowledged problem.
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness.
