Proceedings of the First Workshop on Storytelling

Margaret Mitchell, Ting-Hao ‘Kenneth’ Huang, Francis Ferraro, Ishan Misra (Editors)


Anthology ID:
W18-15
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
NAACL | Story-NLP | WS
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/W18-15
DOI:
10.18653/v1/W18-15
Bib Export formats:
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PDF:
https://aclanthology.org/W18-15.pdf

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Proceedings of the First Workshop on Storytelling
Margaret Mitchell | Ting-Hao ‘Kenneth’ Huang | Francis Ferraro | Ishan Misra

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Linguistic Features of Helpfulness in Automated Support for Creative Writing
Melissa Roemmele | Andrew Gordon

We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users’ modifications to generated sentences, which can be used to quantify their helpfulness in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation.

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Towards Controllable Story Generation
Nanyun Peng | Marjan Ghazvininejad | Jonathan May | Kevin Knight

We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.