William Ma
2019
Guided Neural Language Generation for Automated Storytelling
Prithviraj Ammanabrolu
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Ethan Tien
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Wesley Cheung
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Zhaochen Luo
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William Ma
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Lara Martin
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Mark Riedl
Proceedings of the Second Workshop on Storytelling
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. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.
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Co-authors
- Prithviraj Ammanabrolu 1
- Ethan Tien 1
- Wesley Cheung 1
- Zhaochen Luo 1
- Lara Martin 1
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