Towards generating stylistic dialogues for narratives using data-driven approaches

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Authors: Xu, W., Hargood, C., Tang, W. and Charles, F.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 11318 LNCS

Pages: 462-472

eISSN: 1611-3349

ISBN: 9783030040277

ISSN: 0302-9743

DOI: 10.1007/978-3-030-04028-4_53

© Springer Nature Switzerland AG 2018. Recently, there has been a renewed interest in generating dialogues for narratives. Within narrative dialogues, their structure and content are essential, though style holds an important role as a mean to express narrative dialogue through telling stories. Most existing approaches of narrative dialogue generation tend to leverage hand-crafted rules and linguistic-level styles, which lead to limitations in their expressivity and issues with scalability. We aim to investigate the potential of generating more stylistic dialogues within the context of narratives. To reach this, we propose a new approach and demonstrate its feasibility through the support of deep learning. We also describe this approach using examples, where story-level features are analysed and modelled based on a classification of characters and genres.

The data on this page was last updated at 04:55 on May 22, 2019.