It is expected that the generalized templates can constrain the search space in generation and the model achieves higher fluency by loss of expressive power.
we find that only 0.48%, 1.01%, and 1.20% of the unigrams, bigrams, and trigrams are unique in the stories generated by the GE-Seq2Seq model, while the percentages are 3.16%, 15.33%, and 29.67% in the stories generated by our proposed model.