Automatic Novel Writing: A Status Report

Klein, Sheldon; Aeschlimann, John F.; Balsiger, David F.; Converse, Steven L.; Court, Claudine; Foster, Mark S.; Lao, Robin; Oakley, John D.; Smith, J. G.

doi:10.1515/9783110837537-015

Cited by 37 publications

(25 citation statements)

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“…Automatic Story Generation Computer-aided story generation has been a source of interest since the early days of NLP. Classical AI algorithms relied on symbolic and logical planning and graph construction (Klein et al, 1973;Meehan, 1977;Turner, 1993;Riedl and Young, 2006). Statistical methods have also been proposed (McIntyre and Lapata, 2009;Li and Riedl, 2015;Gatt and Krahmer, 2018).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Hierarchical Story Infilling

Ippolito¹,

Grangier²,

Callison-Burch³

et al. 2019

Proceedings of the First Workshop on Narrative Understanding

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Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity. * Work performed while a Google Student Researcher.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Hierarchical Story Infilling

Ippolito¹,

Grangier²,

Callison-Burch³

et al. 2019

Proceedings of the First Workshop on Narrative Understanding

View full text Add to dashboard Cite

show abstract

“…Automatic story generation has a long history, with early work based primarily on hand-written rules (Klein et al, 1973;Meehan, 1977;Dehn, 1981;Turner, 1993). Subsequent methods were based on planning from artificial intelligence (Theune et al, 2003;Oinonen et al, 2006;Riedl and Young, 2010) and, more recently, data-driven methods have been developed (McIntyre and Lap ata, 2010;Elson, 2012;Daza et al, 2016;Roemmele and Gordon, 2015;Clark et al, 2018a;Martin et al, 2018;Fan et al, 2018b;Yao et al, 2019;Fan et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Generating Diverse Story Continuations with Controllable Semantics

Ding

Yu³

et al. 2019

Proceedings of the 3rd Workshop on Neural Generation and Translation

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We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values;(2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system.

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Section: Related Workmentioning

confidence: 99%

Proceedings of the First Workshop on Narrative Understanding

2019

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Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called negative-critical sequence training, which is proposed to eliminate the need of training a separate critic for estimating baseline. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline-recurrent attention-based bidirectional MLE-trained neural language model.

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Automatic Novel Writing: A Status Report

Cited by 37 publications

References 0 publications

Unsupervised Hierarchical Story Infilling

Unsupervised Hierarchical Story Infilling

Generating Diverse Story Continuations with Controllable Semantics

Proceedings of the First Workshop on Narrative Understanding

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