Aleksei Goncharov


2024

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Leveraging Summarization for Unsupervised Dialogue Topic Segmentation
Aleksei Artemiev | Daniil Parinov | Alexey Grishanov | Ivan Borisov | Alexey Vasilev | Daniil Muravetskii | Aleksey Rezvykh | Aleksei Goncharov | Andrey Savchenko
Findings of the Association for Computational Linguistics: NAACL 2024

Traditional approaches to dialogue segmentation perform reasonably well on synthetic or written dialogues but suffer when dealing with spoken, noisy dialogs. In addition, such methods require careful tuning of hyperparameters. We propose to leverage a novel approach that is based on dialogue summaries. Experiments on different datasets showed that the new approach outperforms popular state-of-the-art algorithms in unsupervised topic segmentation and requires less setup.

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On the Way to Lossless Compression of Language Transformers: Exploring Cross-Domain Properties of Quantization
Nikita Martynov | Aleksei Goncharov | Gleb Kumichev | Evgeniy Egorov | Stanislav Vladimirovich Pavlov | Mikhail Sergeevich Durinov | Aleksandr Sergeevich Zuev | Egor Anatolievich Filimonov
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Modern Transformers achieved impressive results on various Natural Language Processing tasks over the last few years. The one downside of this success is the size of these models. Huge capacity, which sometimes surpasses billions of parameters, improves generalization abilities, but makes it difficult to employ. Developing field of model compression seeks to reduce the model size and inference latency. This research focuses on one of the compression techniques — Post-Training Quantization. We present a methodology to effectively quantize at least 95% of Transformer weights and corresponding activations to INT8 without any access to task-specific data so the drop in performance does not exceed 0.02%. Furthermore, we provide intriguing observations that reflect cross-domain nature of some of the quantization properties.