Essential Quality Validation for NLP Chatbot Systems
School
San Jose State University**We aren't endorsed by this school
Course
CMPE 287
Subject
Information Systems
Date
Dec 12, 2024
Pages
20
Uploaded by CaptainInternet15950
Quality Validation forNLP-Based Machine Learning and Intelligent Chatbot SystemsBy: Jerry Gao, Professor
Accuracy- Evaluate system accuracy based on well-defined test models and accuracy metrics to ensure that the system can accurately process the given input image/video data, and generate accurate chat responses in diverse domain knowledge, various subject contents, different languages, and linguistics using diverse tests with diverse chat patterns and flows.Consistency- Evaluate system consistency based on well-defined test models and consistency metrics to ensure that the system can consistently process and understand diverse NLP and rich media inputs as well as client attentions, and generate consistent chat responses in diverse domain knowledge, various subject contents, different language, and linguistics using diverse tests with chat patterns and flows.Correctness- Evaluate system correctness based on well-defined test models and metrics to evaluate how well a chat system can correctly process and understand NLP and/or rich media inputs, and generate correct responses in domain subjects and contents, and language linguistics.User satisfactory – Evaluate system user satisfactory based on well-defined metrics in different perspectives, including user reviews, ranking, chat interactions, session successful rate, and goal completion.ReliabilityPerformanceCorrectnessAvailabilityConsistencyAccuracyScalabilitySatisfactorySecurityIntelligent Chatbot System - Non-Function Quality Assurance ParametersRelevanceQ&ASubject
Intelligent Chatbot System - Non-Function Quality Assurance ParametersReliabilityPerformanceCorrectnessAvailabilityConsistencyAccuracyScalabilitySatisfactorySecurityAvailability - Evaluate system availability based on well-defined system availability metrics at different levels, including underlying cloud infrastructure, supporting platform environment, and targeted chat application SaaS, as well as user-oriented chat SaaS.Security- Evaluate system security based on well-defined security metrics to check chat system security in different perspectives, including its underlying cloud infrastructure, supporting platform environment, client application SaaS, and user authentication, and end-to-end chat sessions.Reliability- Evaluate system reliability based on well-defined reliability metrics at different levels, including underlying cloud infrastructure, deployed and hosted platform environment, and chat application SaaS.Scalability- Evaluate system scalability based on well-defined scalability metrics in different perspectives, including deployed cloud-based infrastructure, hosted platform, intelligent chat application, large-scale chatting data volume, and user-oriented large-scale accesses.Performance - Evaluate system performance based on well-defined system performance metrics in system-and-user response time, NLP-based and/or rich media input processing time, and chat response generation time.RelevanceQ&ASubject