Manvel Avetisian


Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking
Anton Alekseev | Zulfat Miftahutdinov | Elena Tutubalina | Artem Shelmanov | Vladimir Ivanov | Vladimir Kokh | Alexander Nesterov | Manvel Avetisian | Andrei Chertok | Sergey Nikolenko
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Medical data annotation requires highly qualified expertise. Despite the efforts devoted to medical entity linking in different languages, available data is very sparse in terms of both data volume and languages. In this work, we establish benchmarks for cross-lingual medical entity linking using clinical reports, clinical guidelines, and medical research papers. We present a test set filtering procedure designed to analyze the “hard cases” of entity linking approaching zero-shot cross-lingual transfer learning, evaluate state-of-the-art models, and draw several interesting conclusions based on our evaluation results.

Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Artem Vazhentsev | Gleb Kuzmin | Artem Shelmanov | Akim Tsvigun | Evgenii Tsymbalov | Kirill Fedyanin | Maxim Panov | Alexander Panchenko | Gleb Gusev | Mikhail Burtsev | Manvel Avetisian | Leonid Zhukov
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. Most of the works on modeling the uncertainty of deep neural networks evaluate these methods on image classification tasks. Little attention has been paid to UE in natural language processing. To fill this gap, we perform a vast empirical investigation of state-of-the-art UE methods for Transformer models on misclassification detection in named entity recognition and text classification tasks and propose two computationally efficient modifications, one of which approaches or even outperforms computationally intensive methods.

RuCCoN: Clinical Concept Normalization in Russian
Alexandr Nesterov | Galina Zubkova | Zulfat Miftahutdinov | Vladimir Kokh | Elena Tutubalina | Artem Shelmanov | Anton Alekseev | Manvel Avetisian | Andrey Chertok | Sergey Nikolenko
Findings of the Association for Computational Linguistics: ACL 2022

We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals. It contains over 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology. We provide train/test splits for different settings (stratified, zero-shot, and CUI-less) and present strong baselines obtained with state-of-the-art models such as SapBERT. At present, Russian medical NLP is lacking in both datasets and trained models, and we view this work as an important step towards filling this gap. Our dataset and annotation guidelines are available at

Towards Computationally Feasible Deep Active Learning
Akim Tsvigun | Artem Shelmanov | Gleb Kuzmin | Leonid Sanochkin | Daniil Larionov | Gleb Gusev | Manvel Avetisian | Leonid Zhukov
Findings of the Association for Computational Linguistics: NAACL 2022

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. One of such problems is the excessive computational resources required to train an acquisition model and estimate its uncertainty on instances in the unlabeled pool. We propose two techniques that tackle this issue for text classification and tagging tasks, offering a substantial reduction of AL iteration duration and the computational overhead introduced by deep acquisition models in AL. We also demonstrate that our algorithm that leverages pseudo-labeling and distilled models overcomes one of the essential obstacles revealed previously in the literature. Namely, it was shown that due to differences between an acquisition model used to select instances during AL and a successor model trained on the labeled data, the benefits of AL can diminish. We show that our algorithm, despite using a smaller and faster acquisition model, is capable of training a more expressive successor model with higher performance.


Transformer Models for Drug Adverse Effects Detection from Tweets
Pavel Blinov | Manvel Avetisian
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

In this paper we present the drug adverse effects detection system developed during our participation in the Social Media Mining for Health Applications Shared Task 2020. We experimented with transfer learning approach for English and Russian, BERT and RoBERTa architectures and several strategies for regression head composition. Our final submissions in both languages overcome average F1 by several percents margin.