no code implementations • Findings (ACL) 2022 • Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
1 code implementation • LREC 2022 • Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
Medical data annotation requires highly qualified expertise.
no code implementations • 20 Nov 2023 • Andrey Bout, Alexander Podolskiy, Sergey Nikolenko, Irina Piontkovskaya
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data.
no code implementations • 14 Nov 2023 • Konstantin Yakovlev, Gregory Polyakov, Ilseyar Alimova, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL).
1 code implementation • 14 Nov 2023 • Konstantin Yakovlev, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models.
no code implementations • 14 Nov 2023 • Laida Kushnareva, Tatiana Gaintseva, German Magai, Serguei Barannikov, Dmitry Abulkhanov, Kristian Kuznetsov, Eduard Tulchinskii, Irina Piontkovskaya, Sergey Nikolenko
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated.
no code implementations • 9 Oct 2023 • Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan, Sergey Nikolenko
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.
no code implementations • 18 Jul 2023 • Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.
no code implementations • ICCS: International Conference on Computational Science 2023 • Vadim Lomshakov, Sergey Kovalchuk, Maxim Omelchenko, Sergey Nikolenko, Artem Aliev
We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow.
Ranked #1 on Code Generation on CoNaLa
1 code implementation • NeurIPS 2023 • Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Serguei Barannikov, Irina Piontkovskaya, Sergey Nikolenko, Evgeny Burnaev
Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society.
no code implementations • 19 May 2023 • Nikita Sorokin, Dmitry Abulkhanov, Sergey Nikolenko, Valentin Malykh
We consider the clone detection and information retrieval problems for source code, well-known tasks important for any programming language.
no code implementations • 19 May 2023 • Ivan Sedykh, Dmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko, Valentin Malykh
Code search is an important task that has seen many developments in recent years.
1 code implementation • 26 Apr 2023 • Aleksei Shabanov, Aleksei Tarasov, Sergey Nikolenko
Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well.
Ranked #2 on Metric Learning on In-Shop
no code implementations • 30 Nov 2022 • Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Serguei Barannikov, Irina Piontkovskaya, Sergey Nikolenko, Evgeny Burnaev
We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT.
no code implementations • 25 Jul 2022 • Qi Yang, Sergey Nikolenko, Alfred Huang, Aleksandr Farseev
In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale.
1 code implementation • 24 Jun 2022 • Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, Sergey Nikolenko
Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.
Ranked #1 on Open Information Extraction on LSOIE
no code implementations • 25 Nov 2021 • Anton Alekseev, Elena Tutubalina, Sejeong Kwon, Sergey Nikolenko
In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.
no code implementations • COLING 2020 • Andrey Savchenko, Anton Alekseev, Sejeong Kwon, Elena Tutubalina, Evgeny Myasnikov, Sergey Nikolenko
Understanding image advertisements is a challenging task, often requiring non-literal interpretation.
1 code implementation • 25 Sep 2020 • Mikhail Romanov, Nikolay Patatkin, Anna Vorontsova, Sergey Nikolenko, Anton Konushin, Dmitry Senyushkin
Our work shows that a model trained on this data along with conventional datasets can gain accuracy while predicting correct scene geometry.
no code implementations • 17 Jun 2020 • Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
1 code implementation • 7 Apr 2020 • Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov, Andrey Sakhovskiy, Valentin Malykh, Sergey Nikolenko
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
1 code implementation • CVPR 2020 • Ivan Anokhin, Pavel Solovev, Denis Korzhenkov, Alexey Kharlamov, Taras Khakhulin, Alexey Silvestrov, Sergey Nikolenko, Victor Lempitsky, Gleb Sterkin
We present the high-resolution daytime translation (HiDT) model for this task.
no code implementations • 16 Aug 2019 • Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov, Eugene Beloded
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.
1 code implementation • 7 Aug 2019 • Dmitry Nikulin, Anastasia Ianina, Vladimir Aliev, Sergey Nikolenko
We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i. e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents.
no code implementations • WS 2019 • Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
1 code implementation • ACL 2019 • Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alex Tselousov, er, Thomas Wolf
Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.
no code implementations • 5 May 2019 • Alexander Rakhlin, Aleksei Tiulpin, Alexey A. Shvets, Alexandr A. Kalinin, Vladimir I. Iglovikov, Sergey Nikolenko
Breast cancer is one of the main causes of death worldwide.
3 code implementations • 29 Nov 2018 • Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alan Aspuru-Guzik, Alex Zhavoronkov
Generative models are becoming a tool of choice for exploring the molecular space.
no code implementations • 28 Nov 2018 • Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).