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Automatic dialog systems have become a mainstream part of online customerservice. Many such systems are built, maintained, and improved by customerservice specialists, rather than dialog systems engineers and computerprogrammers. As conversations between people and machines become commonplace,it is critical to understand what is working, what is not, and what actions canbe taken to reduce the frequency of inappropriate system responses. Theseanalyses and recommendations need to be presented in terms that directlyreflect the user experience rather than the internal dialog processing.  This paper introduces and explains the use of Actionable ConversationalQuality Indicators (ACQIs), which are used both to recognize parts of dialogsthat can be improved, and to recommend how to improve them. This combinesbenefits of previous approaches, some of which have focused on producing dialogquality scoring while others have sought to categorize the types of errors thedialog system is making.  We demonstrate the effectiveness of using ACQIs on LivePerson internal dialogsystems used in commercial customer service applications, and on the publiclyavailable CMU LEGOv2 conversational dataset (Raux et al. 2005). We report onthe annotation and analysis of conversational datasets showing which ACQIs areimportant to fix in various situations.  The annotated datasets are then used to build a predictive model which uses aturn-based vector embedding of the message texts and achieves an 79% weightedaverage f1-measure at the task of finding the correct ACQI for a givenconversation. We predict that if such a model worked perfectly, the range ofpotential improvement actions a bot-builder must consider at each turn could bereduced by an average of 81%.

Actionable Conversational Quality Indicators for Improving Task-Oriented Dialog Systems