Papers by Alexey Goncharov
TopicNet: Making Additive Regularisation for Topic Modelling Accessible (2020.lrec-1)
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Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova, Eugenia Veselova, Alexey Goncharov, Evgeny Egorov
| Challenge: | TopicNet is a Python module for topic modeling. |
| Approach: | They introduce a Python module for topic modeling that brings regularization topic modeling to non-specialists using a general-purpose language. |
| Outcome: | The proposed module aims to bring topic modeling to non-specialists using a general-purpose language. |
Complexity-aware fine-tuning (2026.findings-eacl)
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| Challenge: | General-purpose Large Language Models (LLMs) are often fine-tuned through supervised fine- tuning (SFT) to enhance performance in specific domains. |
| Approach: | They propose a novel approach that uses reasoning only for complex data identified by entropy to refine large language models. |
| Outcome: | The proposed model outperforms the standard SFT approach while using 81% less data. |
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024.findings-naacl)
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Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko
| Challenge: | Existing methods to segment textual data are difficult to handle for noisy spoken dialogues. |
| Approach: | They propose to leverage dialogue summaries for unsupervised topic segmentation . they show that the new approach outperforms state-of-the-art methods in unsupervised segmentation and requires less setup . |
| Outcome: | The proposed approach outperforms state-of-the-art methods in unsupervised topic segmentation and requires less setup. |