Title | ||
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Plan-CVAE - A Planning-Based Conditional Variational Autoencoder for Story Generation. |
Abstract | ||
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Story generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated stories and those written by human writers, in this paper, we propose a planning-based conditional variational autoencoder, namely Plan-CVAE, which first plans a keyword sequence and then generates a story based on the keyword sequence. In our method, the keywords planning strategy is used to improve thematic consistency while the CVAE module allows enhancing wording diversity. Experimental results on a benchmark dataset confirm that our proposed method can generate stories with both thematic consistency and wording novelty, and outperforms state-of-the-art methods on both automatic metrics and human evaluations. |
Year | DOI | Venue |
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2020 | 10.1007/978-3-030-63031-7_8 | CNCL |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
22 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Lin Wang | 1 | 0 | 0.34 |
Juntao Li | 2 | 9 | 6.62 |
Dongyan Zhao | 3 | 998 | 96.35 |
Rui Yan | 4 | 961 | 76.69 |