Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Table 6: The values of hyperparameters that result in the best performance on the validation set.
['parameter', 'DailyDialog', 'SwitchBoard']
To approximate the best values of the hyperparameters, we perform a grid search, in which one parameter is varied while all others are fixed. The search was carried out in the multi-task learning setup on the dataset for the UO problem-domain on DailyDialog. For each variation of hyperparamter values, we train the model on the training set of UO and evaluate it on its validation set. The parameter values that result in the highest respective performance was chosen for evaluation on the test set. The values for the number of epochs and batch size were chosen to trade off the running time and memory consumption of the training. For the experiments on DailyDialog we set the maximum number of epochs to 20 and the batch size to 128, while for SwitchBoard the maximum number of epochs is set to 10 and the batch size to 16. Note that hyperparameter tuning has not been performed for SwitchBoard. Thus for the experiments on SwitchBoard mostly the same hyperparameters as those used for the experiments on DailyDialog are used, with the exception of the batch size and the number of epochs. The optimization is performed by Adam with its default parameter values except for the learning rate. We train the model on the shuffled batches of training data. The model is evaluated on the validation set at each epoch. The model with the best performance on the validation set is chosen for the evaluation on the test set. The training procedure is accelerated by the usage of a Tesla P100 GPU running with CUDA v.10.1, while the model is implemented in the pytorch7 framework version 1.1.0.