Answerability: A custom metric for evaluating chatbot performance

Abstract

Most commercial conversational AI products in domains spanning e-commerce, health care, finance, and education involve a hierarchy of NLP models that perform a variety of tasks such as classification, entity recognition, question-answering, sentiment detection, semantic text similarity, and so on. Despite our understanding of each of the constituent models, we often do not have a clear view as to how these models affect the overall platform metrics. To bridge this gap, we define a metric known as answerability, which penalizes not only irrelevant or incorrect chatbot responses but also unhelpful responses that do not serve the chatbot's purpose despite being correct or relevant. Additionally, we describe a formula-based mathematical framework to relate individual model metrics to the answerability metric. We also describe a modeling approach for predicting a chatbot's answerability to a user question and its corresponding chatbot response.