Automated Creation of an Intent Model for Conversational Agents
Conversational Agents (CA) are increasingly being deployed by organizations to provide round-the-clock support and to increase customer satisfaction. All CA have one thing in common despite the differences in their design: they need to be trained with users’ intents and corresponding training senten...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2164401 |
| _version_ | 1797684784550379520 |
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| author | Alberto Benayas Miguel Angel Sicilia Marçal Mora-Cantallops |
| author_facet | Alberto Benayas Miguel Angel Sicilia Marçal Mora-Cantallops |
| author_sort | Alberto Benayas |
| collection | DOAJ |
| description | Conversational Agents (CA) are increasingly being deployed by organizations to provide round-the-clock support and to increase customer satisfaction. All CA have one thing in common despite the differences in their design: they need to be trained with users’ intents and corresponding training sentences. Access to proper data with acceptable coverage of intents and training sentences is a big challenge in CA deployment. Even with the access to the past conversations, the process of discovering intents and training sentences manually is not time and cost-effective. Here, an end to end automated framework that can discover intents and their training sentences in conversation logs to generate labeled data sets for training intent models is presented. The framework proposes different feature engineering techniques and leverages dimensionality reduction methods to assemble the features, then applies a density-based clustering algorithm iteratively to mine even the least common intents. Finally, the clustering results are automatically labeled by the final algorithm. |
| first_indexed | 2024-03-12T00:34:47Z |
| format | Article |
| id | doaj.art-0d7d207a4e55410ba15b3b387ab77eb5 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:34:47Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-0d7d207a4e55410ba15b3b387ab77eb52023-09-15T10:01:05ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452023-12-0137110.1080/08839514.2022.21644012164401Automated Creation of an Intent Model for Conversational AgentsAlberto Benayas0Miguel Angel Sicilia1Marçal Mora-Cantallops2University of AlcaláUniversity of AlcaláUniversity of AlcaláConversational Agents (CA) are increasingly being deployed by organizations to provide round-the-clock support and to increase customer satisfaction. All CA have one thing in common despite the differences in their design: they need to be trained with users’ intents and corresponding training sentences. Access to proper data with acceptable coverage of intents and training sentences is a big challenge in CA deployment. Even with the access to the past conversations, the process of discovering intents and training sentences manually is not time and cost-effective. Here, an end to end automated framework that can discover intents and their training sentences in conversation logs to generate labeled data sets for training intent models is presented. The framework proposes different feature engineering techniques and leverages dimensionality reduction methods to assemble the features, then applies a density-based clustering algorithm iteratively to mine even the least common intents. Finally, the clustering results are automatically labeled by the final algorithm.http://dx.doi.org/10.1080/08839514.2022.2164401 |
| spellingShingle | Alberto Benayas Miguel Angel Sicilia Marçal Mora-Cantallops Automated Creation of an Intent Model for Conversational Agents Applied Artificial Intelligence |
| title | Automated Creation of an Intent Model for Conversational Agents |
| title_full | Automated Creation of an Intent Model for Conversational Agents |
| title_fullStr | Automated Creation of an Intent Model for Conversational Agents |
| title_full_unstemmed | Automated Creation of an Intent Model for Conversational Agents |
| title_short | Automated Creation of an Intent Model for Conversational Agents |
| title_sort | automated creation of an intent model for conversational agents |
| url | http://dx.doi.org/10.1080/08839514.2022.2164401 |
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