Improving Results on Russian Sentiment Datasets

• URL: http://arxiv.org/abs/2007.14310v1
• Date: Tue, 28 Jul 2020 15:29:19 GMT
• Title: Improving Results on Russian Sentiment Datasets
• Authors: Anton Golubev and Natalia Loukachevitch
• Abstract summary: We 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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