Building Performance Functions | The PARADISE model uses stepwise multiple linear regression to predict subjective user satisfaction based on measures representing the performance dimensions of task success, dialogue quality, and dialogue efficiency, resulting in a predictor function of the following form: |
Building Performance Functions | Stepwise linear regression produces coefficients (wi) describing the relative contribution of each predictor to the user satisfaction. |
Building Performance Functions | Using stepwise linear regression , we computed a predictor function for each of the subj ective measures that we gathered during our study: the mean score for each of the individual user-satisfaction categories (Table 4), the mean score across the whole questionnaire (the last line of Table 4), as well as the difference between the users’ emotional states before and after the study (the last line of Table 5). |
Discussion | (2008) for linear regression models similar to those presented here were between 0.22 and 0.57. |
Introduction | PARADISE uses stepwise multiple linear regression to model user satisfaction based on measures representing the performance dimensions of task success, dialogue quality, and dialogue efficiency, and has been applied to a wide range of systems (e. g., Walker et al., 2000; Litman and Pan, 2002; Moller et al., 2008). |
Expt. 2: Predicting Pairwise Preferences | We reuse the linear regression framework from Section 2 and predict pairwise preferences by predicting two absolute scores (as before) and comparing them.6 |
Introduction | We first explore the combination of traditional scores into a more robust ensemble metric with linear regression . |
Regression-based MT Quality Prediction | We follow a similar idea, but use a regularized linear regression to directly predict human ratings. |