Learning to Predict Explainable Plots for Neural Story Generation
Table 4: Comparison with variational inference on the ROCStories dataset. We use “VI” to denote variational inference (see Section 3.2).
['Training', 'BLEU', 'METEOR', 'ROUGE-L']
Note that Eq. The expectation in Eq. Therefore, we are interested in the expectation with respect to the planning model. We find that training by variation inference deteriorates the performance of our model. One possible reason is that in variational inference the generation model is not exposed to the outlines generated by the planning model during training. During inference, however, the generation model takes the outline output by the planning model as one input. This mismatch between training and inference seems to be harmful for the NSG task. In contrast, the training objective in Eq.