Modern machine learning techniques in the natural language processing domain
can be used to automatically generate scripts for goal-oriented dialogue
systems. The current article presents a general framework for studying the
automatic generation of scripts for goal-oriented dialogue systems. A method
for preprocessing dialog data sets in JSON format is described. A comparison is
made of two methods for extracting user intent based on BERTopic and latent
Dirichlet allocation. A comparison has been made of two implemented algorithms
for classifying statements of users of a goal-oriented dialogue system based on
logistic regression and BERT transformer models. The BERT transformer approach
using the bert-base-uncased model showed better results for the three metrics
Precision (0.80), F1-score (0.78) and Matthews correlation coefficient (0.74)
in comparison with other methods.

自然语言处理领域的现代机器学习技术可用于自动生成目标导向对话系统的脚本。本文提出了一个研究自动生成目标导向对话系统脚本的通用框架，并描述了一种用于预处理 JSON 格式对话数据集的方法。比较了基于 BERTopic 和潜在狄利克雷分配的两种提取用户意图的方法，并比较了基于逻辑回归和 BERT transformer 模型的两种分类用户语句的算法实现。与其他方法相比，使用 bert-base-uncased 模型的 BERT transformer 方法在精确度（0.80），F1 分数（0.78）和马修斯相关系数（0.74）等三个指标上表现更好。