Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems

Leonid Legashev, Alexander Shukhman, Vadim Badikov
12/15/2023
cs.AI

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Abstract

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.

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Cite this paper

@article{legashev2023algorithms,
  title  = {Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems},
  author = {Leonid Legashev and Alexander Shukhman and Vadim Badikov},
  year   = {2023},
  eprint = {2312.09658},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.09658v2}
}

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