The development of large and super-large language models, such as GPT-3, T5,
Switch Transformer, ERNIE, etc., has significantly improved the performance of
text generation. One of the important research directions in this area is the
generation of texts with arguments. The solution of this problem can be used in
business meetings, political debates, dialogue systems, for preparation of
student essays. One of the main domains for these applications is the economic
sphere. The key problem of the argument text generation for the Russian
language is the lack of annotated argumentation corpora. In this paper, we use
translated versions of the Argumentative Microtext, Persuasive Essays and UKP
Sentential corpora to fine-tune RuBERT model. Further, this model is used to
annotate the corpus of economic news by argumentation. Then the annotated
corpus is employed to fine-tune the ruGPT-3 model, which generates argument
texts. The results show that this approach improves the accuracy of the
argument generation by more than 20 percentage points (63.2\% vs. 42.5\%)
compared to the original ruGPT-3 model.

本文介绍了如何利用 RuBERT fine-tuning 技术以及经过语言标注的经济新闻语料库来优化 ruGPT-3 模型，从而提高俄文下生成文字的准确率。