3 citations · 16 across the 15 of their papers we have counts for
16 papers
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
Daryna Dementieva, Daniil Moskovskiy, David Dale +1
Text detoxification is the task of transferring the style of text from toxic to neutral. While here are approaches yielding promising results in monolingual setup, e.g., (Dale et a…
Don't lose the message while paraphrasing: A study on content preserving style transfer
Nikolay Babakov, David Dale, Ilya Gusev +2
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,…
Efficient GPT Model Pre-training using Tensor Train Matrix Representation
Viktoriia Chekalina, Georgii Novikov, Julia Gusak +2
Large-scale transformer models have shown remarkable performance in language modelling tasks. However, such models feature billions of parameters, leading to difficulties in their…
Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval
Viktoriia Chekalina, Alexander Panchenko
In this paper, we present a submission to the Touche lab's Task 2 on Argument Retrieval for Comparative Questions. Our team Katana supplies several approaches based on decision tre…
Active Learning for Abstractive Text Summarization
Akim Tsvigun, Ivan Lysenko, Danila Sedashov +9
Construction of human-curated annotated datasets for abstractive text summarization (ATS) is very time-consuming and expensive because creating each instance requires a human annot…
Error syntax aware augmentation of feedback comment generation dataset
Nikolay Babakov, Maria Lysyuk, Alexander Shvets +2
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a…
Multiverse: Multilingual Evidence for Fake News Detection
Daryna Dementieva, Mikhail Kuimov, Alexander Panchenko
Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. It is becoming essential to develop fake news detec…
Studying the role of named entities for content preservation in text style transfer
Nikolay Babakov, David Dale, Varvara Logacheva +2
Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. Ho…
Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models
Daniil Moskovskiy, Daryna Dementieva, Alexander Panchenko
Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are designed to work in…
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.…
Detecting Text Formality: A Study of Text Classification Approaches
Daryna Dementieva, Nikolay Babakov, Alexander Panchenko
Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language…
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…