Goal - oriented conversational bot for employment domain

• Autorzy: Drozda Paweł , Żmijewski Tomasz , Osowski Maciej , Krasnodębska Aleksandra , Talun Arkadiusz

Identyfikatory

DOI

10.31648/ts.9333

• Języki publikacji: EN

Abstrakty

EN

This paper focuses of the implementation of the goal – oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7.

Słowa kluczowe

EN

chatbot; Deep Q Network; DQN; goal; oriented bot; Natural Language Processing; NLP

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2023
• Tom: nr 26(1)
• Strony: 111--123
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Drozda Paweł

• Katedra Metod Matematycznych Informatyki, Wydział Matematyki i Informatyki, ul. Słoneczna 54, 10-710 Olsztyn
• Emplocity SA, Warszawa

• https://orcid.org/0000-0003-3163-9408

autor

Żmijewski Tomasz

• Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn

• https://orcid.org/0009-0004-7157-9192

autor

Osowski Maciej

• Emplocity SA, Warszawa

• https://orcid.org/0000-0003-0277-3798

autor

Krasnodębska Aleksandra

• Emplocity SA, Warszawa

• https://orcid.org/0009-0004-1702-0865

autor

Talun Arkadiusz

• Emplocity SA, Warszawa

Bibliografia

• ARRUDA D., MARINHO M., SOUZA E., WANDERLEY F. 2019. A Chatbot for Goal-Oriented Requirements Modeling. Computational Science and Its Applications – ICCSA 2019. Lecture Notes in Computer Science, 11622. https://doi.org/10.1007/978-3-030-24305-0_38
• BHARTI U., BAJAJ D., BATRA H., LALIT S., LALIT S., GANGWANI A. 2020. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, p. 870-875. https://doi.org/10.1109/ICCES48766.2020.9137944
• BRONWYN J., RHIANNE J. 2019. Public Service Chatbots: Automating Conversation with BBC News. Digital Journalism, 7 (8): 1032-1053. https://doi.org/10.1080/21670811.2019.1609371
• DROZDA P., TALUN A., BUKOWSKI L. 2019. Emplobot – Design of the System. In Proceedings of the 28th International Workshop on Concurrency, Specification and Programming, Olsztyn.
• GERVASI V., ZOWGHI D. 2005. Reasoning about inconsistencies in natural language requirements. ACM Transactions on Software Engineering and Methodology (TOSEM), 14(3): 277-330. https://doi.org/10.1145/1072997.1072999
• HAMDAQA M., METZ L.A.P., QASSE I. 2020. IContractML: A domain-specific language for modeling and deploying smart contracts onto multiple blockchain platforms. In Proceedings of the 12th System Analysis and Modelling Conference, p. 34-43. https://doi.org/10.48550/arXiv.2103.09314
• HOLDEN THORP H. 2023 ChatGPT is fun, but not an author. Science 379, 313-313. https://doi.org/10.1126/science.adg7879
• KUMAR H.V., NAGARAJ J., IRFAN M., MAHESHWARI N., BALUSANI P., CHATTERJEE P., SRINIVASA G. 2018. PESUBot: An Empathetic Goal Oriented Chatbot. International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, p. 1083-1089. https://doi.org/10.1109/ICACCI.2018.8554916
• LE N., SIDDIQUE A.B., JAMOUR F., OYMAK S., HRISTIDIS V. 2021. Predictable and adaptive goal-oriented dialog policy generation. IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, p. 40-47. https://doi.org/10.1109/ICSC50631.2021.00012
• LI X., CHEN Y.N., LI L., GAO J., CELIKYILMAZ A. 2017. End-to-end task-completion neural dialogue systems. arXiv preprintarXiv: 1703.01008. https://doi.org/10.48550/arXiv.1703.01008
• LIU J., PAN F., LUO L. 2020. Gochat: Goal-oriented chatbots with hierarchical reinforcement learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 1793-1796. https://doi.org/10.1145/3397271.3401250
• LONE M.B., NAZIR N., KAUR N., PRADEEP D., ASHRAF A.U., ASRAR UL HAQ P., DAR N.B., SARWAR A., RAKHRA M., DAHIYA O. 2022. Self-Learning Chatbots using Reinforcement Learning. 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, p. 802-808. https://doi.org/10.1109/ICIEM54221.2022.9853156
• MURALI S.R., RANGREJI S., VINAY S., SRINIVASA G. 2020. Automated NER, sentiment analysis and toxic comment classification for a goal-oriented chatbot. In 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS) Fez, Morocco, p. 1-7. https://doi.org/10.1109/ICDS50568.2020.9268680
• PRASETYO P.K., ACHANANUPARP P., LIM E.P. 2020. Foodbot: A Goal-Oriented Just-in-Time Healthy Eating Interventions Chatbot. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth ‘20). Association for Computing Machinery, New York, p. 436-439. https://doi.org/10.1145/3421937.3421960
• QASSE I., MISHRA S., HAMDAQA M. 2021. iContractBot: A chatbot for smart contracts’ specification and code generation. In 2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE), p. 35-38.
• SALVI S., GEETHA V., SOWMYA KAMATH S. 2019. Jamura: A Conversational Smart Home Assistant Built on Telegram and Google Dialogflow. TENCON 2019 IEEE Region 10 Conference (TENCON), Kochi, India, p. 1564-1571. https://doi.org/10.1109/TENCON.2019.8929316
• SCHAUB L.P., VAUDAPIVIZ C. 2019. Goal-oriented dialog systems and Memory: an overview. 9th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Poznań.
• SERBAN I., SORDONI A., LOWE R., CHARLIN L., PINEAU J., COURVILLE A., BENGIO Y. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In Proceedings of the AAAI conference on artificial intelligence, p. 3295–3301. https://doi.org/10.48550/arXiv.1605.06069
• SOLANKI R.K., RAJAWAT A.S., GADEKAR A.R., PATIL M.E. 2023. Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines, p. 285-309. https://doi.org/10.4018/978-1-6684-7164-7.ch013
• SUHEL S.F., SHUKLA V.K., VYAS S., MISHRA V. 2020. Conversation to Automation in Banking Through Chatbot Using Artificial Machine Intelligence Language. 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, p. 611-618. https://doi.org/10.1109/ICRITO48877.2020.9197825