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2020 | OriginalPaper | Buchkapitel

Improving Results on Russian Sentiment Datasets

verfasst von : Anton Golubev, Natalia Loukachevitch

Erschienen in: Artificial Intelligence and Natural Language

Verlag: Springer International Publishing

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Abstract

In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets. We compare two variants of Russian BERT and show that for all sentiment tasks in this study the conversational variant of Russian BERT performs better. The best results were achieved by BERT-NLI model, which treats sentiment classification tasks as a natural language inference task. On one of the datasets, this model practically achieves the human level .

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Titel: Improving Results on Russian Sentiment Datasets
verfasst von: Anton Golubev
Natalia Loukachevitch
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Natural Language
Print ISBN: 978-3-030-59081-9

Electronic ISBN: 978-3-030-59082-6

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-59082-6_8

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