Cue Me In: Content-Inducing Approaches to Interactive Story Generation (2020)

Abstract: Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.

## introduction automatic story generation requires composing a coherent and fluent passage of text about a sequence of events. prior studies on story generation mostly focused on symbolic planning @xcite @xcite or case-based reasoning @xcite that heavily relied on manual knowledge engineering. recent state-of-the-art methods for story generation @xcite are based on sequence-to-sequence models @xcite ) that generate a story in one go. in this setting, the user has little control over the generated story. on the other hand, when humans write, they incrementally edit and refine the text they produce. motivated by this, rather than generating the entire story at once, we explore the problem of interactive story generation. in this setup, a user can provide the model mid-level sentence abstractions in the form of cue phrases as the story is being generated. cue phrases enable the user to inform the system of what they want to happen next in the story and have more control over what is being generated. to achieve our goal, this paper primarily focuses on approaches for smoothly and effectively incorporating user-provided cues. the schematic in fig. 1 illustrates this scenario: the system generates the story one sentence at a time, and the user guides the content of the next sentence using cue phrases. we note that the generated sentences need to fit the context, and also be semantically related to the provided cue phrase. a fundamental advantage of using this framework as opposed to a fully automated one is that it can provide an interactive interface for human users to incrementally supervise the generation by giving signals to the model throughout the story generation process. this human-computer collaboration can result in generating richer and personalized stories. in particular, this field of research can be used in addressing the literacy needs of learners with disabilities and enabling children to explore creative writing at an early age by crafting their own stories. in this paper, we present two content-inducing approaches based on the transformer network @xcite for interactively incorporating external knowledge when automatically generating stories. here, our external knowledge is in the form of cue phrases provided by the user to enable interaction, but can readily be replaced with knowledge accessible through other means foot_0 . specifically, our models fuse information from the story context and cue phrases through a hierarchical attention mechanism. the first approach, cued writer, employs two independent encoders (for incorporating context and cue phrases) and an additional attention component to capture the semantic agreement between the cue phrase and output sentence. the second approach, relevance cued writer, additionally measures the relatedness between the context and cue phrase through a contextcue multi-head unit. in both cases, we introduce different attention units in a single end-to-end neural network. our automatic and human evaluations demonstrate that the presented models outperform strong baselines and can successfully incorporate cues in generated stories. this capability is one step closer to an interactive setup, and unlike one-shot generation, it lets users have more control over the generation. our contributions are twofold: ## related work automatic story generation is a longstanding problem in ai, with early work dating back to the 1970s based on symbolic planning @xcite @xcite and casebased reasoning using ontologies @xcite . @xcite extended prior works toward learning domain models (via corpus and/or crowdsourcing) to support open story generation about any topic. with the advent of deep learning there has been a major shift towards using seq2seq models @xcite for various text generation tasks, including storytelling @xcite @xcite . however, these models often fail to ensure coherence in the generated story. to address this problem, @xcite incorporated entities given their vector representations, which get updated as the story unfolds. similarly, @xcite proposed a character-centric story generation by learning character embeddings directly from the corpus. @xcite followed a twostep process to first generate the premise and then condition on that to generate the story. @xcite proposed a multi-pass cvae to improve wording diversity and content consistency. previous work has explored the potential of creative writing with a machine in the loop. @xcite found that people generally enjoy collaborating with a machine. traditional methods proposed to write stories collaboratively using a case-based reasoning architecture @xcite . recent work @xcite extended this to find relevant suggestions for the next sentence in a story from a large corpus. other methods proposed gui and tools to facilitate co-creative narrative generation @xcite . unlike us, these approaches explore the value of and tools for interaction rather than designing methods for incorporating user input into the model. another line of research decomposes story generation into two steps: story plot planning and plot-to-surface generation. previous work produces story-plans based on sequences of events @xcite @xcite , critical phrases @xcite or both events and entities @xcite . @xcite al. (2019) a major focus of these works is on generating a coherent plan for generating the story. in contrast, our contribution is complementary since we do not focus on planning but on generation. we present approaches to effectively incorporate external knowledge in the form of cue-phrases during generation, and conduct extensive experiments to compare our models with those of @xcite by modifying them to work in our setup. ## interactive story generation we design models to generate a story one sentence at a time. given the generated context so far (as a sequence of tokens) @xmath0, ..., x t }, and the cue phrase for the next sentence @xmath1, ..., c k }, our models generate the tokens of the next sentence of the story @xmath2, ..., y m }. we train the models by minimizing the cross-entropy loss: here, θ refers to model parameters. note that when generating the n-th sentence, the model takes the first n -1 sentences in the story as the context along with the cue phrase. in the rest of this section, we describe our two novel content-inducing approaches for addressing the interactive story generation task: the cued writer, and the relevance cued writer. these models share an overall encoder-decoder based architecture shown in fig. 2 . they adopt a dual encoding approach where two separate but architecturally similar encoders are used for encoding the context (context encoder represented in the green box) and the cue phrase (cue encoder represented in the purple box). both these encoders advise the decoder (represented in the blue box), which in turn generates the next sentence. the two proposed models use the same encoding mechanism (described in § 3.1) and differ only in their decoders (described in § 3.2). ## qualitative results and error analysis not-logically-consistent: avery received a homework assignment due in two weeks. he immediately read it. when he turned it in, he made schedule. he completed tasks and turned it in time. when he finished early, he was disappointed. we also manually reviewed 50 stories, generated from our models and analyzed common errors. ## conclusion and future work this paper explored the problem of interactive storytelling, which leverages human and computer collaboration for creative language generation. we presented two content-inducing approaches that take user-provided inputs as the story progresses and effectively incorporate them in the generated text. experimental results show that our methods outperform competitive baselines. however, there are several other significant aspects to be considered in story generation, such as modeling of discourse relations, and representation of key narrative elements, which lie beyond the scope of this investigation. also, while we received encouraging feedback from users on this setup during the interactive evaluation, we did not explore important questions about user interfaces, design, and human computer interaction. future work can explore these questions and also explore other forms of natural language interaction.