WriterForcing: Generating more interesting story endings
Table 4: We measure the performance of the models using an automated Story-Cloze classifier which compares the outputs of model with the outputs of IE model.
['[BOLD] Model', '[BOLD] Story-cloze']
The Keyphrase + ITF loss model outperforms all models on both the diversity and Story-Cloze metrics. Hence, we select Keyphrase + ITF loss model as the best model in further discussions. As an ablation study, we run the Keyphrase loss model with the MLE loss instead of the ITF loss. We find that this model performs poorly than then its version with the ITF loss but still performs quite better than the Seq2Seq model. Also we note that the diversity obtained in Keyphrase + ITF loss model is greater than the Seq2Seq + ITF model and the Keyphrase loss model without ITF. It shows that a combination of both, Keyphrase attention loss and ITF loss, achieves better performance than these components by themselves.