3 citations · 3 across the 3 of their papers we have counts for
4 papers · 1 filter
Text Detoxification using Large Pre-trained Neural Models
David Dale, Anton Voronov, Daryna Dementieva +4
We present two novel unsupervised methods for eliminating toxicity in text. Our first method combines two recent ideas: (1) guidance of the generation process with small style-cond…
Methods for Detoxification of Texts for the Russian Language
Daryna Dementieva, Daniil Moskovskiy, Varvara Logacheva +4
We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for process…
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
Nikolay Babakov, Varvara Logacheva, Olga Kozlova +2
Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or…
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova +7
Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with acti…