“聊天机器人交流”作为数字交流系统中语言学研究的对象

Владимировна Киселева, Анна Андреевна Смирнова, Нэлла Аркадьевна Трофимова
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摘要

介绍。本文的作者从语言学的角度考虑了数字化领域中有趣的技术之一——聊天机器人。这是语言学家特别感兴趣的话题,因为数字环境的积极发展导致了语音产生和语音感知的新手段和新形式的出现,它们有自己独特的特征、模式和结构规则。在本工作的框架内,网络话语系统中的言语行为是至关重要的:研究对象位于几个学科(IT、广告和公关)的交叉点,但从语言学的角度来看,它实际上没有被考虑。方法和来源。为了澄清科学工作中的一些理论不准确之处,对英语和俄语网络语料库的文本进行了语料库分析。英语网络语料库:“NOW语料库”(网络新闻)、“iWeb语料库”、“GloWbE语料库”(全球网络英语)俄语网络语料库:“rutenen2011”和GIKRYA。以各种社交网络和即时通讯工具中的英语和俄语聊天机器人的语言材料为基础,分析聊天机器人交流中言语行为构建的特点。结果和讨论。语料库文本分析有助于从语言学的角度考虑研究对象,并用语言学数据补充现有知识。基于两种语言的经验材料分析可以让我们比较,识别聊天机器人交流文本的特征,追踪语音认知感知的异同,也可以发现语音行为构建中的错误。数据的系统化和对“聊天机器人交流”这一概念的更详细的分析,将有助于在未来为机器人创建具有最终场景的通用语言认知模型,这将有助于避免错误,并使交流行为尽可能成功。聊天机器人从交际功能的角度来看,在组织言语行为时,必须使用特殊的规则和语言模型,才能使言语行为取得成功。就像人工智能领域的自我学习机器人是在语言学家的积极参与下创造出来的一样,社交网络和即时通讯工具中的聊天机器人也必须遵守统一的语言规律。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
“Chatbot Communication” as an Object of Linguistic Research in the System of Digital Communications
Introduction. The authors of the article consider one of the interesting technologies in the digitalization segment – a chatbot – from a linguistic point of view. The topic is of particular interest to linguists, since the active development of the digital environment leads to the emergence of new means and forms of speech production and speech perception, with their own distinctive features, patterns and rules of construction. Speech acts in the Internet discourse system within the framework of this work are of key importance: the object of study is located at the intersection of several disciplines (IT, Advertising and PR), but from a linguistic point of view it is practically not considered.Methodology and sources. To clarify some theoretical inaccuracies in the scientific work, corpus analysis of the texts of English-language and Russian-language web corpus is used. English-language web corpus: “NOW corpus” (News on the Web), “iWeb corpus”, “GloWbE corpus” (Global Web-based English). Russian-language web corpora: “ruTenTen2011” and GIKRYA. The analysis of the features of the construction of speech acts in chatbot communication is carried out on the basis of the language material of English and Russian chatbots in various social networks and instant messengers.Results and discussion. Corpus text analysis helps to consider the object of study from a linguistic point of view and supplement existing knowledge with linguistic data. The analysis of empirical material based on two languages allows us to compare, identify the characteristics of chatbot communication texts, trace similarities and differences in the cognitive perception of speech, and also find errors in the construction of speech acts.Conclusion. Systematization of data and a more detailed analysis of such a concept as “chatbot communication” will help in the future to create universal linguistic-cognitive models for bots with a final scenario, which will help to avoid mistakes and make the communication act as successful as possible. Chatbots, from the point of view of the communicative function, when organizing speech acts, must use special rules and linguistic models in order to bring the speech act to success. Just as self-learning bots in the category of artificial intelligence are created with the active participation of linguists, so chatbots in social networks and instant messengers must be subject to uniform linguistic laws.
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