Abstract:User quality judgements can show a bewildering amount of variation that is difficult to capture using traditional quality prediction approaches. Using clustering, an exploratory statistical analysis technique, we reanalysed the data set of a Wizard-of-Oz experiment where 25 users were asked to rate the dialogue after each turn. The sparse data problem was addressed by careful a priori parameter choices and comparison of the results of different cluster algorithms. We found two distinct classes of users, positi… Show more
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