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Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice
RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.ORCID-id: 0000-0001-7879-4371
Lund University, Sweden.
Lund University, Sweden.
NordAxon AB, Sweden.
Vise andre og tillknytning
2022 (engelsk)Inngår i: Proceedings - 1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, s. 22-32Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Due to the migration megatrend, efficient and effective second-language acquisition is vital. One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews. The action team elicited a set of 38 requirements for which we designed corresponding automated test cases for 15 of particular interest to the evolving solution. Our results show that six of the test case designs can detect meaningful differences between candidate models. While quality assurance of natural language processing applications is complex, we provide initial steps toward an automated framework for machine learning model selection in the context of an evolving conversational agent. Future work will focus on model selection in an MLOps setting.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2022. s. 22-32
Emneord [en]
action research, AI quality, conversational agent, generative dialog model, requirements engineering, software testing, Learning algorithms, Natural language processing systems, Quality assurance, Software agents, Conversational agents, Dialogue models, Megatrends, Model Selection, Requirement engineering, Software testings, Swedishs
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-59869DOI: 10.1145/3522664.3528592Scopus ID: 2-s2.0-85133467455ISBN: 9781450392754 (tryckt)OAI: oai:DiVA.org:ri-59869DiVA, id: diva2:1685105
Konferanse
1st International Conference on AI Engineering - Software Engineering for AI, CAIN 2022, 16 May 2022 through 17 May 2022
Tilgjengelig fra: 2022-08-01 Laget: 2022-08-01 Sist oppdatert: 2024-01-10bibliografisk kontrollert

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Borg, MarkusTomaszewski, Piotr

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RefereraExporteraLink to record
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  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Annet språk
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