Search Results for author: Artem Shelmanov

Found 12 papers, 4 papers with code

Uncertainty Estimation of Transformer Predictions for Misclassification Detection

1 code implementation ACL 2022 Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov

Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.

Active Learning Adversarial Attack Detection +5

Generating Lexical Representations of Frames using Lexical Substitution

no code implementations PaM 2020 Saba Anwar, Artem Shelmanov, Alexander Panchenko, Chris Biemann

We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs.


NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment Analysis

1 code implementation EMNLP 2021 Nikolay Arefyev, Dmitrii Kharchev, Artem Shelmanov

While Masked Language Models (MLM) are pre-trained on massive datasets, the additional training with the MLM objective on domain or task-specific data before fine-tuning for the final task is known to improve the final performance.

Domain Adaptation Sentiment Analysis

Towards Computationally Feasible Deep Active Learning

no code implementations7 May 2022 Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.

Active Learning Text Classification

Neural Entity Linking: A Survey of Models Based on Deep Learning

no code implementations31 May 2020 Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov, Alexander Panchenko, Chris Biemann

This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language processing.

Entity Embeddings Entity Linking

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

no code implementations LREC 2020 Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko

We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al. (2018), enabling WSD in these languages.

Word Embeddings Word Sense Disambiguation

A Dataset for Noun Compositionality Detection for a Slavic Language

1 code implementation WS 2019 Dmitry Puzyrev, Artem Shelmanov, Alex Panchenko, er, Ekaterina Artemova

This paper presents the first gold-standard resource for Russian annotated with compositionality information of noun compounds.

Towards the Data-driven System for Rhetorical Parsing of Russian Texts

no code implementations WS 2019 Artem Shelmanov, Dina Pisarevskaya, Elena Chistova, Svetlana Toldova, Maria Kobozeva, Ivan Smirnov

Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank {--} first Russian corpus annotated within RST framework {--} are presented.

Classification General Classification +1

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