Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation

Goldfarb-Tarrant, Seraphina; Feng, Haining; Peng, Nanyun

doi:10.18653/v1/n19-4016

Cited by 54 publications

(57 citation statements)

References 9 publications

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“…Early narrative prose generation systems (Meehan, 1977;Callaway and Lester, 2001;Riedl and Young, 2004) relied on graph-based planning formalisms and custom rules to structure their narratives, while story graphs have been used for interactive storytelling (Riedl and Bulitko, 2013). More recent work uses deep learning to generate stories by training neural models with limited context (Peng et al, 2018;Fan et al, 2018;Goldfarb-Tarrant et al, 2019) and structured knowledge, either external (Mao et al, 2019;Guan et al, 2020;Goldfarb-Tarrant et al, 2020) or derived (Yao et al, 2019;Fan et al, 2019). Compared to the datasets studied in those works, our STORIUM dataset contains much longer stories with built-in structural annotations written in natural language in the form of cards (Table 2).…”

Section: Related Workmentioning

confidence: 99%

STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Akoury¹,

Shu-fan²,

Whiting³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.

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Section: Related Workmentioning

confidence: 99%

STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Akoury¹,

Shu-fan²,

Whiting³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…We use the ROCStories (Mostafazadeh et al, 2016) dataset to generate stories using the Plan and Write model outlined by Yao et al (2019); Goldfarb-Tarrant et al (2019). We introduce a two step pipeline procedure where we fine-tune a pre-trained GPT2 (Radford et al, 2018) model on titles and storyline from the training set to generate a storyline given a title (Row 1 Table 10).…”

Section: Story Generationmentioning

confidence: 99%

Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

Chakrabarty¹,

Muresan²,

Peng³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile, goes beyond plain expressions to give readers new insights and inspirations. We tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019), on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% 1 of the times, and three baseline systems including a recent metaphor generation model 71% 2 of the times when compared pairwise. 3 We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges. * The research was conducted when the author was at USC/ISI. 1 We average 32.6% and 41.3% for 2 humans. 2 We average 82% ,63% and 68% for three baselines. 3 The simile in the title is generated by our best model. Input: Generating similes effortlessly, output: Generating similes like a Pro.

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“…Zhang et al (2016) proposed to detect the writers' purpose in the revised sentences. Goldfarb-Tarrant et al (2019) developed a collaborative humanmachine story-writing tool that assists writers with story-line planning and story-detail writing. The assistant and feedback generally improved the user engagement and the quality of generated text in those works.…”

Section: Related Workmentioning

confidence: 99%

ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

2019

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

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In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewriting, and text style transfer. Our tool is characterized by two features, i) recording of word-level revision histories and ii) flexible auxiliary edit support and feedback to annotators. The text rewriting assist and traceable rewriting history are potentially beneficial to the future research of natural language generation.

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Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation

Cited by 54 publications

References 9 publications

STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation

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