학술논문

Quality Assurance of a German COVID-19 Question Answering Systems using Component-based Microbenchmarking
Document Type
Conference
Source
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. :1561-1564
Subject
ai system engineering
component-oriented validation
covid-19
quality assurance
question answering
Language
English
Abstract
Question Answering (QA) has become an often used method to retrieve data as part of chatbots and other natural-language user interfaces. In particular, QA systems of official institutions have high expectations regarding the answers computed by the system, as the provided information might be critical. In this demonstration, we use the official COVID-19 QA system that was developed together with the German Federal government to provide German citizens access to data regarding incident values, number of deaths, etc. To ensure high quality, a component-based approach was used that enables exchanging data between QA components using RDF and validating the functionality of the QA system using SPARQL. Here, we will demonstrate how our solution enables developers of QA systems to use a descriptive approach to validate the quality of their implementation before the system's deployment and also within a live environment.

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