Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.09658 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912087160127488 |
|---|---|
| author | Legashev, Leonid Shukhman, Alexander Badikov, Vadim |
| author_facet | Legashev, Leonid Shukhman, Alexander Badikov, Vadim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09658 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems Legashev, Leonid Shukhman, Alexander Badikov, Vadim Artificial Intelligence 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. |
| title | Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2312.09658 |