Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022)

Abstract: Stories or narratives are comprised of a sequence of events. To compose interesting stories, professional writers often leverage a creative writing technique called *flashback* that inserts past events into current storylines as we commonly observe in novels and plays. However, it is challenging for machines to generate *flashback* as it requires a solid understanding of event **temporal order** (e.g. *feeling hungry* before *eat*, not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in **narrative order**. Two major issues in existing systems that exacerbate the challenges: 1) temporal bias in pertaining and story datasets that leads to monotonic event temporal orders; 2) lack of explicit guidance that helps machines decide where to insert *flashbacks*. We propose to address these issues using structured storylines to encode events and their pair-wise temporal relations (before, after and vague) as **temporal prompts** that guide how stories should unfold temporally. We leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines and stories end-to-end. Evaluation results show that the proposed method can generate more interesting stories with *flashbacks* while maintaining textual diversity, fluency, and temporal coherence.

## introduction flashback is a popular creative writing technique that brings the readers from the present moment to the past via inserting earlier events in order to provide background or context of the current narrative @xcite @xcite . for example, in figure 1a , the "ghost" in shakespeare's play hamlet instruments a flashback by interrupting the main narrative and describing a historical event to the audience that hamlet's father was killed by the current king rather than a snake. 1 code, data and trained models are available here: https: //github.com/pluslabnlp/flashback_gen (a) a flashback example from william shakespeare's famous play hamlet (in plain english). red text indicates past events. similarly, consider the pair of two-sentence stories in figure 1b . both stories are composed of the same events with the temporal order "lost consciousness" before "woke up in the hospital." in story (1), seeing [e1], readers can make a relatively easy educated guess of @xcite , but it is more subtle in story (2) as there are many different ways to end up in a hospital. by showing the ending event first, the flashback in story (2) creates suspense that makes the following sentences less predictable, and thus arouses readers' curiosity and makes the reading more interesting. while human writers are capable of maneuver-ing event temporal orders to compose coherent and interesting stories, it remains challenging for machines. the challenge is partially attributed to data bias. @xcite shows that the pattern in story (1) is dominant in human-written texts, where neighboring events with before temporal relations (i.e., narrative order indicates temporal order) occur 60 -70% of the time. this is also manifested in our experiments with vanilla language models amplifying this ratio and producing more than 80% before relations for neighboring events in the generated stories. furthermore, current stateof-the-art story generation systems that incorporate event representations usually assume event temporal order follows narrative order @xcite . there are no explicit prompts in these systems that help determine when flashback should be used, leaving models to produce dull stories consisting of event sequences with monotonic before relations. to facilitate more effective flashback, we propose to incorporate temporal prompts in an endto-end story generation framework inspired by the plan-and-write paradigm @xcite @xcite , where machines first learn to plot a storyline, and then generate the story based on the storyline. specifically, we encode predefined event temporal prompts in structured storylines. as the bottom block of figure 2 shows, a structured storyline contains two components: 1) event representations where an event trigger ("grabbed") and two arguments ("she" and "the dog") are extracted from the original story sentences; and 2) temporal prompts: the temporal order between neighboring events, e.g. event 1: ("she", "grabbed", "the dog") is after event 2: ("white snow", "blanketed", "the ground"). by training our storyline generation model with these predefined pair-wise temporal relations, models capture how neighboring events are temporally related to each other; while during storyline decoding, supplying predefined temporal prompts can guide models to generate reasonable narratives with desirable event temporal orders. prior works @xcite build the storyline and story models separately, which creates a discrepancy where gold storylines are used during training, but predicted storylines are used during inference. to mitigate this training-inference discrepancy, we leverage reinforcement learning (rl) to train our systems end-to-end. it enables the story model to train on generated storylines and updates the storyline model with the feedback from the story model. our experimental results show that the rl-based models can leverage temporal prompts more effectively, resulting in more effective flashback generation and more interesting stories. we summarize the contributions of this paper as follows: 1) to facilitate effective flashback, we propose to leverage structured storylines with temporal prompts to arrange events in story generation. 2) we integrate reinforcement learning in our story generation pipeline, which can help models better leverage temporal prompts. 3) we test our framework on two open-domain story datasets and show more effective flashbacks and increased interest level while maintaining fluency and temporal commonsense in the generated stories. to our best knowledge, this is a pioneering study on flashbacks in neural story generation. ## background and task definitions in this section, we describe the key components: events and temporal prompts in our proposed structured storylines, and then define the plan-and-write generation task. ## framework for flashback generation in this section, we first provide an overview of the plan-and-write story generation system and introduce a vanilla version of the end-to-end training method. then we describe the details of our key contribution of leveraging event temporal prompts to generate flashbacks. after that, we discuss pretraining structured storylines with self-labeled data and incorporating reinforcement learning to jointly train our end-to-end models. ## experimental setup in this section, we start by describing our research objectives, then we describe our data, evaluation metrics, experimental designs and implementation details aiming to achieve these objectives. the overall research objective is to measure the impact of using temporal prompts in structured storylines. specifically, can after successfully induce flashbacks? if so, does that contribute to the interest level of the generated stories while maintaining the overall quality of the texts? ## results and analysis the main results for rocstories and writing-prompts are shown in bleu and rouge l . for rocstories, the proposed models perform on-par with vanilla-gen and temporal-bart while outperforming megatron, which generates the shortest stories among all compared models. for writingprompts, contentplanning performs the best partially due to its usage of bart-large models. the overall performances across these three types of automatic metrics suggest that using temporal prompts in the plan-and-write framework can produce stories with high textual quality. ## error analysis in temporal diversity. input 2 [s1] anna had just gotten her hair permed. prompt before before after before @xcite she was excited to see how it looked. our work differs from this line of research by using temporal prompts with pretrained language models to generate integrated flashback in stories. @xcite studies flashback in game narrative generation, which is remotely related to our work. plan-and-write framework has been shown to be an effective method to enhance the explainability and controllability of story generation. @xcite enables machines to produce a sequence of keywords prior to generating stories. follow-up works leverage commonsense or external knowledge to enhance the quality of stories @xcite @xcite . @xcite is one of our compared works that incorporates srl extracted event representations in storylines and train models with several event-related decoding objectives. our work differs from it by explicitly encoding temporal prompts in event plots that facilitates flashback. structured representation such as discourse structure @xcite , story keywords @xcite and event/plot graph @xcite have been widely used in story generation to enable models to output diverse stories, but they are remotely related to our flashback generation task. reinforcement learning has also been explored in two-stage story generation such as @xcite and @xcite . our motivation of using rl-based generation is to enhance the effectiveness of temporal prompts. event temporal reasoning helps the construction of the temporal prompts. it has been studied in story understanding @xcite , information extraction @xcite @xcite , qa @xcite , and event generation @xcite . our proposed method is the pioneer work to introduce event temporal prompts in flashback generation. ## conclusions we propose to generate flashbacks in stories by encoding temporal prompts in structured storylines. experimental and evaluation results show our proposed systems can produce fluent, temporally coherent, and more interesting stories. future work can focus on improving temporal prompts so that they handle minority cases (i.e. prompts with one or more after indicators) more effectively. designing more powerful rewards in reinforcement learning is another promising research direction.