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Towards Generating Stylistic Dialogues for Narratives using Data-Driven Approaches.

Xu, W., Hargood, C., Tang, W. and Charles, F., 2018. Towards Generating Stylistic Dialogues for Narratives using Data-Driven Approaches. In: ICIDS 2018: International Conference for Interactive Digital Storytelling, 5 - 8 December 2018, Trinity College Dublin, Ireland.

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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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Conference program:
Uncontrolled Keywords:Dialogue Generation; Interactive Narratives; Dialogue Style; Neural Networks
Group:Faculty of Science & Technology
ID Code:31506
Deposited By: Symplectic RT2
Deposited On:04 Dec 2018 15:53
Last Modified:14 Mar 2022 14:13


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