Plan-and-Write: Towards Better Automatic Storytelling

Yao, Lili; Peng, Nanyun; Weischedel, Ralph; Knight, Kevin; Zhao, Dongyan; Yan, Rui

doi:10.1609/aaai.v33i01.33017378

Cited by 279 publications

(394 citation statements)

References 10 publications

Supporting

Mentioning

393

Contrasting

Unclassified

Order By: Relevance

“…Most content selection models train the selector with heuristic rules (Hsu et al, 2018;Li et al, 2018;Gehrmann et al, 2018;Yao et al, 2019;Moryossef et al, 2019), which fail to fully capture the relation between selection and generation. Mei et al (2016); ; ; Li et al (2018) "soft-select" word or sentence embeddings based on a gating function.…”

Section: Related Workmentioning

confidence: 99%

Select and Attend: Towards Controllable Content Selection in Text Generation

Shen¹,

Suzuki

Inui

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

View full text Add to dashboard Cite

Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.

show abstract

Section: Related Workmentioning

confidence: 99%

Select and Attend: Towards Controllable Content Selection in Text Generation

Shen¹,

Suzuki

Inui

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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). In concurrent work, Gupta et al (2019) also propose methods to generate more diverse and interesting story endings, albeit without control variables.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Due to their ability to reason over sequences of textual input, sequence-to-sequence networks are typically used to perform open-world story generation. To better help these networks maintain context over long story sequences, many researchers have chosen to make use of event representations that distill essential information from natural language sentences [4,13,20]. These event representations make story generation easier in that the network only needs to focus on generating essential information.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Approach for Visual Storytelling Using Image Description

Nahian

Tasrin

Gandhi

et al. 2019

Interactive Storytelling

View full text Add to dashboard Cite

One of the primary challenges of visual storytelling is developing techniques that can maintain the context of the story over long event sequences to generate human-like stories. In this paper, we propose a hierarchical deep learning architecture based on encoder-decoder networks to address this problem. To better help our network maintain this context while also generating long and diverse sentences, we incorporate natural language image descriptions along with the images themselves to generate each story sentence. We evaluate our system on the Visual Storytelling (VIST) dataset [7] and show that our method outperforms state-of-the-art techniques on a suite of different automatic evaluation metrics. The empirical results from this evaluation demonstrate the necessities of different components of our proposed architecture and shows the effectiveness of the architecture for visual storytelling.

show abstract

Plan-and-Write: Towards Better Automatic Storytelling

Cited by 279 publications

References 10 publications

Select and Attend: Towards Controllable Content Selection in Text Generation

Select and Attend: Towards Controllable Content Selection in Text Generation

Generating Diverse Story Continuations with Controllable Semantics

A Hierarchical Approach for Visual Storytelling Using Image Description

Contact Info

Product

Resources

About