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20212023
most citedDetecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation

3 citations · 16 across the 15 of their papers we have counts for

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cs.CL2023

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…

cs.CL2023

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,…

cs.AI2023★ 1 cited

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…

cs.IR2023★ 2 cited

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…

cs.CL2023★ 3 cited

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…