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At the heart of improving conversational AI is the open problem of how toevaluate conversations. Issues with automatic metrics are well known (Liu etal., 2016, arXiv:1603.08023), with human evaluations still considered the goldstandard. Unfortunately, how to perform human evaluations is also an openproblem: differing data collection methods have varying levels of humanagreement and statistical sensitivity, resulting in differing amounts of humanannotation hours and labor costs. In this work we compare five differentcrowdworker-based human evaluation methods and find that different methods arebest depending on the types of models compared, with no clear winner across theboard. While this highlights the open problems in the area, our analysis leadsto advice of when to use which one, and possible future directions.

Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents