Papers by Nikita Khromov
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |
ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts (2022.emnlp-demos)
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Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova, Dmitry V. Dylov, Igor Galitskiy, Artem Shelmanov
| Challenge: | Currently, the framework supports text classification, sequence tagging, and seq2seq tasks. |
| Approach: | They propose an open-source framework for active learning annotation in natural language processing that provides an easy-to-deploy GUI annotation tool directly in the Jupyter IDE. |
| Outcome: | The proposed framework reduces computational overhead and duration of AL iterations and increases annotated data reusability. |