Outline-Conditioned Generation with Dynamic Plot State Tracking

PLOTMACHINES: Outline-Conditioned Generation with Dynamic Plot State Tracking Hannah Rashkin1, Asli Celikyilmaz2, Yejin Choi1;3, Jianfeng Gao2 1 Paul G. Allen School of Computer Science & Engineering, University of Washington 2 Microsoft Research, Redmond, WA, USA 3 Allen Institute for Artificial Intelligence, Seattle, WA, USA fhrashkin,yejing@cs.washington.edu, faslicel,jfgaog@microsoft.com Abstract • big bird's birthday celebration Story • cookie monster eats We propose the task of outline-conditioned Outline • roller skating rink • big birthday cake story generation: given an outline as a set of Plot dynamics i phrases that describe key characters and events ℙ = paragraph i to appear in a story, the task is to generate a Outline-conditioned Story Generation coherent narrative that is consistent with the It is Big Bird's birthday, and he goes to the roller skating rink with his friends. provided outline. This task is challenging as Back at Sesame Street, Maria and Susan take out the big 1 birthday cake and leave it on a table. the input only provides a rough sketch of the ℙ Cookie Monster sees the cake, but instead of eating it and spoiling the party, he eats a chair and other things all plot, and thus, models need to generate a story over Sesame Street. by interweaving the key points provided in the Big Bird and the other skaters return to Sesame Street outline. This requires the model to keep track and are shocked at what Cookie Monster ate, though the of the dynamic states of the latent plot, condi- cake is safe. 2 Gina and Count Von Count presents the cake to Big Bird. tioning on the input outline while generating ℙ It has 548 candles even though Big Bird is 6 years old. At the end, when Gina announces the sponsors, Cookie the full story. We present PLOTMACHINES, Monster eats them along with his cake. a neural narrative model that learns to transform an outline into a coherent story by track- Figure 1: An outline (input) paired with a story (output) ing the dynamic plot states. In addition, we from the Wikiplots training set. Plot elements from the enrich PLOTMACHINES with high-level dis- outline can appear and reappear non-linearly through- course structure so that the model can learn out the plot, as shown in plot dynamics graph. Com- different writing styles corresponding to dif- posing stories from an outline requires keeping track ferent parts of the narrative. Comprehensive of how outline phrases have been used while writing. experiments over three fiction and non-fiction datasets demonstrate that large-scale language models, such as GPT-2 and GROVER, despite to generate a coherent narrative that is consistent their impressive generation performance, are with the provided outline. This task is challenging not sufficient in generating coherent narratives for the given outline, and dynamic plot state as the input provides only the rough elements of 1 tracking is important for composing narratives the plot . Thus, the model needs to flesh out how with tighter, more consistent plots. these plot elements will intertwine with each other across different parts of the story. The flowchart in 1 Introduction Figure1 demonstrates an example of a latent plot Composing a story requires a complex planning structure: different key phrases from the outline process. First, the writer starts with a rough sketch appear and re-appear jointly throughout different of what key characters and events the story will sentences and paragraphs. Notably, the way that contain. Then, as they unfold the story, the writer outline points are interwoven needs to be deter- must keep track of the elaborate plot that weaves mined dynamically based on what’s already been together the characters and events in a coherent and composed while also staying true to the original consistent narrative. outline and overall narrative structure. We study this complex storytelling process by We present PLOTMACHINES, a novel narrative formulating it as the task of outline-conditioned transformer that simulates the outline-conditioned story generation, illustrated in Figure1. Given an outline, a set of phrases describing key char- 1Here, we define plot as the main sequence of events in acters and events to appear in a story, the task is the story. 4274 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 4274–4295, November 16–20, 2020. c 2020 Association for Computational Linguistics generation process described above.2 Our model 2 Outline-Conditioned Generation learns to transform an outline into a multi- paragraph story using dynamic memory blocks that The Task: Our primary goal is to design a task keep track of the implicit plot states computed us- for investigating how story generation models can ing the outline and the story generated thus far. We plan long narrative according to controllable story draw inspiration from prior work in dialogue state elements. To that end, we introduce the outline- tracking (Thomson and Young, 2010; Lee, 2013; conditioned story generation task, which takes a Chao and Lane, 2019), entity tracking (Henaff et al., plot outline as input and produces a long, multi- 2017; Bosselut et al., 2018), and memory networks paragraph story. (Sukhbaatar et al., 2015) for keeping track of plot states. We also inform our model with high-level In order to be flexible to multiple forms of con- narrative structure using discourse labels so that it trol that might be required for different down- can learn different styles of writing corresponding stream tasks, we envision plot outlines to be de- to different parts of the narrative (i.e. beginning, fined loosely as lists of an arbitrary number of middle, and end). PLOTMACHINES is, to the best un-ordered plot points that should guide a story of our knowledge, the first model designed to gener- being generated. Plot points could consist of high- ate multi-paragraph stories conditioned on outlines level concepts, low-level events, or even detailed and can be trained end-to-end to learn the latent sentences. For practical reasons, in this work, we plot patterns without explicit plot annotations for limit the scope of plot points to events and phrases supervision. since these can be automatically extracted. Future work could explore alternate methods of defining To support research on outline-conditioned gen- plot outlines, perhaps using an event-based plan- eration, we present three datasets, including both ning systems (Porteous and Cavazza, 2009; Riedl, fiction and non-fiction domains, where multi- 2009; Riedl and Young, 2010; Fan et al., 2019) for paragraph narratives from existing datasets are generating key points. paired with automatically constructed outlines using state-of-the-art key phrase extraction. Impor- More concretely, in this paper, we formulate the tantly, our task formulation of outline-conditioned outline as a list of un-ordered bullet points which generation is general and can be applied to various reflect key phrases to be loosely integrated in the forms of grounded language generation. Compre- output narrative. These plot outlines are inspired, hensive experiments on these datasets demonstrate in part, by previous work in short-form story gen- that recently introduced state-of-the-art large-scale eration tasks that conditioned on storylines (Peng language models such as GPT-2 and GROVER et al., 2018; Yao et al., 2019), which were defined (Radford et al., 2019; Zellers et al., 2019), despite as an ordered list of exactly five single-word points. their impressive generation performance, still strug- We extend this concept to long-form story gener- gle to generate coherent narratives that are consis- ation by defining a plot outline more flexibly as: tent with input outlines. Our experiments indicate an un-ordered list of an arbitrary number of multi- that dynamic plot state tracking is important for word plot elements. An outline also differs from a constructing narratives with tighter and more con- writing prompt, such as those found in other consistent plots compared to competitive baselines. trollable writing tasks (Fan et al., 2018), which are Our main contributions are: (1) a new task for- more abstract and often just a starting point for mulation of outline-conditioned story generation, a story. Unlike a prompt, an outline is a list of (2) the presentation of three new datasets for this concrete points that must appear somewhere in the task, (3) PLOTMACHINES, a novel narrative trans- narrative. former that learns to transform outlines to full One challenge of this task is to create stories stories with dynamic plot state tracking, and (4) that have appropriate discourse and narrative flow. empirical results demonstrating the limitations of A second challenge is for stories to include the state-of-the-art large-scale language models and outline in a natural way. For example, it may be the advantage of PLOTMACHINES compared to appropriate for certain outline points to be used competitive baselines. only later on in the story (e.g. the protagonist dying may be more typically used at the end). 2code available at https://github.com/ hrashkin/plotmachines 4275 Wikiplots Outline: • the rocky horror picture show • convention attendees includes servants (...) # stories : 130k Story: A criminologist narrates the tale of the newly engaged couple, Brad Majors and Janet avg # pars : 3.1 Weiss, who find themselves lost and with a flat tire on a cold and rainy late November evening, data-split : 90/5/5 somewhere near Denton in 1974 (...) WritingPrompts Outline: • found something protruding • geometric shapes glowing • sister kneel- # stories : 300k ing beside • dead bodies everywhere • darkness overwhelmed • firelight flickering (...) avg # pars : 5.9 Story: It was dark and Levi was pretty sure he was lying on his back . There was firelight flickering data-split : 90/5/5 off of what was left of a ceiling .

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