Alexander Panchenko

Prof. Alexander Panchenko

Skoltech
orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2016
Verified
Conference Paper
Open access
APAlexander Panchenko
Alexander Panchenko
Stefano FaralliStefano Faralli
Stefano Faralli
EREugen Ruppert
Eugen Ruppert
SRSteffen Remus
Steffen Remus
HNHubert Naets
Hubert Naets
CBChris Biemann
Chris Biemann

1 January 2021
Verified
Conference Paper
Open access
PDF file
ASArtem Shelmanov
Artem Shelmanov
ETEvgenii Tsymbalov
Evgenii Tsymbalov
DPDmitri Puzyrev
Dmitri Puzyrev
KFKirill Fedyanin
Kirill Fedyanin
APAlexander Panchenko
Alexander Panchenko
MPMaxim Panov
Maxim Panov
Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

17 February 2017
Verified
Book Chapter
Closed access
PDF file
APAlexander Panchenko
Alexander Panchenko
DUDmitry Ustalov
Dmitry Ustalov
NANikolay Arefyev
Nikolay Arefyev
DPDenis Paperno
Denis Paperno
NKNatalia Konstantinova
Natalia Konstantinova
CBChris Biemann
Chris Biemann

1 January 2024
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Conference Paper
Open access
PDF file
VMViktor Moskvoretskii
Viktor Moskvoretskii
ENEkaterina Neminova
Ekaterina Neminova
ALAlina Lobanova
Alina Lobanova
APAlexander Panchenko
Alexander Panchenko
INIrina Nikishina
Irina Nikishina
Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, Alexander Panchenko, Irina Nikishina. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

1 November 2019
Verified
Conference Paper
Closed access
ASArtem Shelmanov
Artem Shelmanov
VLVadim Liventsev
Vadim Liventsev
DKDanil Kireev
Danil Kireev
NKNikita Khromov
Nikita Khromov
APAlexander Panchenko
Alexander Panchenko
DDDmitry V. Dylov
Dmitry V. Dylov
Active learning is a technique that helps to minimize the annotation budget required for the creation of a labeled dataset while maximizing the performance of a model trained on this dataset. It has been shown that active learning can be successfully applied to sequence tagging tasks of text processing in conjunction with deep learning models even when a limited amount of labeled data is available. Recent advances in transfer learning methods for natural language processing based on deep pre-trained models such as ELMo and BERT offer a much better ability to generalize on small annotated datasets compared to their shallow counterparts. The combination of deep pre-trained models and active learning leads to a powerful approach to dealing with annotation scarcity. In this work, we investigate the potential of this approach on clinical and biomedical data. The experimental evaluation shows that the combination of active learning and deep pre-trained models outperforms the standard methods of active learning. We also suggest a modification to a standard uncertainty sampling strategy and empirically show that it could be beneficial for annotation of very skewed datasets. Finally, we propose an annotation tool empowered with active learning and deep pre-trained models that could be used for entity annotation directly from Jupyter IDE.

Verified
Journal Article
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Daryna DementievaDaryna Dementieva
Daryna Dementieva
DMDaniil Moskovskiy
Daniil Moskovskiy
VLVarvara Logacheva
Varvara Logacheva
DDDavid Dale
David Dale
OKOlga Kozlova
Olga Kozlova
APAlexander Panchenko
Alexander Panchenko
We introduce the first study of the automatic detoxification of Russian texts to combat offensive language. This kind of textual style transfer can be used for processing toxic content on social media or for eliminating toxicity in automatically generated texts. While much work has been done for the English language in this field, there are no works on detoxification for the Russian language. We suggest two types of models—an approach based on BERT architecture that performs local corrections and a supervised approach based on a pretrained GPT-2 language model. We compare these methods with several baselines. In addition, we provide the training datasets and describe the evaluation setup and metrics for automatic and manual evaluation. The results show that the tested approaches can be successfully used for detoxification, although there is room for improvement.