Adaptation of distilling knowledge method in Natural Language Processing for sentiment analysis.

  • O. Korovii National Technical University of Ukraine "Kyiv Polytechnic Institute of Igor Sikorsky"
Keywords: BERT, FastText, distill knowledge, neural network, natural language processing, sentiment analysis

Abstract

This paper describes how to adapt an application method of " knowledge distillation " for sentiment analysis for Ukrainian and Russian languages. It is demonstrated how to minimize resources without losing much accuracy, but speeding up the text sentiment recognition, and how to decrease expenses on cloud by using the method of "knowledge distillation". For research we used two types of different neural networks architecture for natural language processing: BERT instead of ensemble models and FastText like a small model. Combination of these two neural networks (BERT as a teacher and FastText as a learner) allowed us to achieve the speedup up to 5 times and without sacrificing much accuracy in sentiment analysis task.

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Published
2021-12-23
How to Cite
Korovii , O. (2021). Adaptation of distilling knowledge method in Natural Language Processing for sentiment analysis . COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (45), 78-83. https://doi.org/10.36910/6775-2524-0560-2021-45-11
Section
Computer science and computer engineering