This research paper discusses various evaluation metrics for conversational AI, including engagement, coherence, and relevance, and proposes a new framework for assessing dialogue systems.
Conversica's guide to conversational AI evaluation metrics covers key performance indicators such as response accuracy, conversation completion rate, and user satisfaction.
The National Institute of Standards and Technology (NIST) provides an overview of dialogue system evaluation, including metrics such as word error rate, semantic accuracy, and dialogue success rate.
This research paper explores the use of human evaluation for assessing conversational AI systems, including metrics such as human-likeness, engagingness, and overall quality.
This comprehensive guide covers various conversational AI metrics, including technical metrics such as latency and throughput, as well as business metrics such as conversion rate and customer satisfaction.
This research paper presents an automated evaluation framework for conversational AI systems, using metrics such as perplexity, BLEU score, and ROUGE score.
This open-source toolkit provides a set of tools and metrics for evaluating conversational AI systems, including dialogue management, natural language understanding, and response generation.
This lecture series from Stanford University covers the design of effective conversational AI evaluation metrics, including the importance of considering user experience, context, and task-oriented evaluation.