Pranav Gupta


2022

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Answerability: A custom metric for evaluating chatbot performance
Pranav Gupta | Anand A. Rajasekar | Amisha Patel | Mandar Kulkarni | Alexander Sunell | Kyung Kim | Krishnan Ganapathy | Anusua Trivedi
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

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 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.