3 citations · 8 across the 5 of their papers we have counts for
6 papers
MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering
Viktoriia Chekalina, Anton Razzhigaev, Albert Sayapin +2
Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world.…
Beyond Plain Toxic: Detection of Inappropriate Statements on Flammable Topics for the Russian Language
Nikolay Babakov, Varvara Logacheva, Alexander Panchenko
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. However, there also e…
Taxonomy Enrichment with Text and Graph Vector Representations
Irina Nikishina, Mikhail Tikhomirov, Varvara Logacheva +3
Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with the hy…
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…