

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

The problem with using auto-SLU-success for training the PDP is that the same data is used to train the auto-SLU-success predictor. Therefore, we used a cross-validation technique (also known as jack-kning) (Weiss Kulikowski, 1991), whereby the training set is partitioned into sets. Three of these sets are used for training and the fourth for testing. The results for the fourth set are noted and the process is repeated, rotating the sets from training to testing. This results in a complete list of predicted auto-SLU-success for the training set. The features for the test set exchanges are derived by training ripper on the whole training set. This process is illustrated in Figure 6.

The following section gives a breakdown of the input features. Section describes the training and results of the auto-SLU-success predictor and Section reports the accuracy results for the PDP.

## 4. The Features
