Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems
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.
Keywords
Cite
@article{arxiv.2312.09658,
title = {Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems},
author = {Leonid Legashev and Alexander Shukhman and Vadim Badikov},
journal= {arXiv preprint arXiv:2312.09658},
year = {2024}
}
Comments
in Russian language This work has been submitted to the IEEE for possible publication