Many storytelling generation problems concern the difficulty to model the sequence of sentences. Language models are generally able to assign high scores to well-formed text, especially in the cases of short texts, failing when they try to simulate human textual inference. Although in some cases output text automatically generated sounds as bland, incoherent, repetitive and unrelated to the context, in other cases the process reveals capability to surprise the reader, avoiding to be boring/predictable, even if the generated text satisfies entailment task requirements. The lyric tradition often does not proceed towards a real logical inference, but takes into account alternatives like the unexpectedness, useful for predicting when a narrative story will be perceived as interesting. To achieve a best comprehension of narrative variety, we propose a novel measure based on two components: inference and unexpectedness, whose different weights can modify the opportunity for readers to have different experiences about the functionality of a generated story. We propose a supervised validation treatment, in order to compare the authorial original text, learned by the model, with the generated one.
Descriptive and Computer Aided Drawing Perspective on an Unfolded Polyhedral Projection Surface
