Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling

We describe the task of sentence expansion and enhancement, in which a sentence provided by a human is expanded in some creative way. The expansion should be understandable, believably grammatical, and highly related to the original sentence. Sentence expansion and enhancement may serve as an authoring tool, or integrate in dynamic media, conversational agents, and advertising. We implement a neural sentence expander, which is trained on sentence compressions generated from a corpus of modern fiction. We modify the objective loss function to support the task by focusing on new words, and decode at test time with controlled curve-like novelty sampling. We run the sentence expander on sentences provided by human subjects and have humans evaluate these expansions. The generation methods are shown to be comparable to, and as well liked as, subjects' original input sentences, and preferred over baselines.