In this study, we aim to find a method to auto-tag sentences specific to a domain. Our training data comprises short conversational sentences extracted from chat conversations between company's customer representatives and web site visitors. We manually tagged approximately 14 thousand visitor inputs into ten basic categories, which will later be used in a transformer-based language model with attention mechanisms for the ultimate goal of developing a chatbot application that can produce meaningful dialogue. We considered three different state-of-the-art models and reported their auto-tagging capabilities. We achieved the best performance with the bidirectional encoder representation from transformers (BERT) model. Implementation of the models used in these experiments can be cloned from our GitHub repository and tested for similar auto-tagging problems without much effort.

中文翻译：

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使用自然语言处理方法自动标记会话短句

在这项研究中，我们的目标是找到一种自动标记特定于域的句子的方法。我们的训练数据包括从公司客户代表和网站访问者之间的聊天对话中提取的简短对话句子。我们手动将大约 14,000 个访问者输入标记为十个基本类别，这些类别稍后将用于具有注意力机制的基于转换器的语言模型，最终目标是开发可以产生有意义对话的聊天机器人应用程序。我们考虑了三种不同的最先进模型并报告了它们的自动标记功能。我们使用来自 Transformers (BERT) 模型的双向编码器表示实现了最佳性能。