使用生成模型和持续行动策略构建叙事

CC-NLG@INLG Pub Date : 2017-09-04 DOI:10.18653/v1/W17-3905
E. Chourdakis, J. Reiss
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引用次数: 4

摘要

本文提出了一种学习如何通过重组以前集合中的句子来生成叙事的方法。给定一个分为9个主题的故事事件语料库,我们近似一个深度强化学习代理策略来重新组合它们以满足叙事结构。我们还建议对这一系统进行评估。评估基于连贯性,兴趣和主题,以确定生成的故事有多大意义,它们有多有趣,并检查是否可以出现新的叙事主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constructing narrative using a generative model and continuous action policies
This paper proposes a method for learning how to generate narrative by recombining sentences from a previous collection. Given a corpus of story events categorised into 9 topics, we approximate a deep reinforcement learning agent policy to recombine them in order to satisfy narrative structure. We also propose an evaluation of such a system. The evaluation is based on coherence, interest, and topic, in order to figure how much sense the generated stories make, how interesting they are, and examine whether new narrative topics can emerge.
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