A Novel Knowledge Plug-In for Incorporating Information About Employability From the O*NET Database Into Conversational Agents
The labor market is a dynamic and rapidly evolving environment. Job positions that require expertise in various sectors often lead candidates to question their suitability. Therefore, it is crucial to furnish them with relevant, accurate, and timely information. In this article, we introduce a knowl...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/396282 |
| Acesso em linha: | http://hdl.handle.net/10261/396282 https://api.elsevier.com/content/abstract/scopus_id/85197561692 |
| Access Level: | acceso abierto |
| Palavra-chave: | Chatbots Human-robot interaction Information extraction Labor market Online enrolling process User experience Virtual assistant |
| Resumo: | The labor market is a dynamic and rapidly evolving environment. Job positions that require expertise in various sectors often lead candidates to question their suitability. Therefore, it is crucial to furnish them with relevant, accurate, and timely information. In this article, we introduce a knowledge plug-in for existing conversational agents designed to address specific queries related to the job market domain. Additionally, we propose an innovative method for dynamically creating diverse grammars and semantically associating users’ questions with a predefined list of domain questions. We present a novel scheme to effectively tackle question-answering tasks in settings with limited resources. Our architecture relies on question-understanding and response-generation modules, both powered by Transformers, the O*NET occupational database, and recommendation engines that suggest training materials. Furthermore, we conducted a user study based on the System Usability Scale (SUS) score, revealing that users highly appreciated the proposed tool. This sentiment was particularly evident when the tool was integrated with other artificial intelligence chatbots capable of handling general information. Simultaneously, our engine adeptly manages information within the investigated domain, providing precise responses and recommendations. This work addresses a critical gap in the delivery of employment information and paves the way for the development of diverse functionalities to assist both candidates and employers. |
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