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

arXiv: https://arxiv.org/abs/2312.09658 · HTML: https://mlsanity.com/paper/2312.09658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Intent Mining from past conversations for conversational agent](https://mlsanity.com/paper/2005.11014/) (2020)

## Abstract (arXiv)

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.