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In recent years, the proliferation of smart mobile devices has lead to thegradual integration of search functionality within mobile platforms. This hascreated an incentive to move away from the "ten blue links'' metaphor, asmobile users are less likely to click on them, expecting to get the answerdirectly from the snippets. In turn, this has revived the interest in QuestionAnswering. Then, along came chatbots, conversational systems, and messagingplatforms, where the user needs could be better served with the system askingfollow-up questions in order to better understand the user's intent. Whiletypically a user would expect a single response at any utterance, a systemcould also return multiple options for the user to select from, based ondifferent system understandings of the user's intent. However, this possibilityshould not be overused, as this practice could confuse and/or annoy the user.How to produce good variable-length lists, given the conflicting objectives ofstaying short while maximizing the likelihood of having a correct answerincluded in the list, is an underexplored problem. It is also unclear how toevaluate a system that tries to do that. Here we aim to bridge this gap. Inparticular, we define some necessary and some optional properties that anevaluation measure fit for this purpose should have. We further show thatexisting evaluation measures from the IR tradition are not entirely suitablefor this setup, and we propose novel evaluation measures that address itsatisfactorily.

Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search