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arXiv · 1909.03480

Story Realization: Expanding Plot Events into Sentences

Prithviraj Ammanabrolu·Ethan Tien·Wesley Cheung·Zhaochen Luo·William Ma·Lara J. Martin·Mark O. Riedl
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Abstract

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events.We provide results---including a human subjects study---for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches.

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Prithviraj Ammanabrolu, Ethan Tien, Wesley Cheung, Zhaochen Luo, William Ma, Lara J. Martin, Mark O. Riedl. 2019-11-21. Story Realization: Expanding Plot Events into Sentences. https://doi.org/10.1609/aaai.v34i05.6232

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