S cript W riter: Narrative-Guided Script Generation (2020)

Abstract: It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications - guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end- users can upload their narratives freely when watching a movie. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.

## introduction narrative is generally understood as a way to tell a story. wordnet defines it as "a message that tells the particulars of an act or occurrence or course of events; presented in writing or drama or cinema or as a radio or television program" 1 . narrative plays an important role in many natural language processing (nlp) tasks. for example, in storytelling, the storyline is a type of narrative, which helps generate coherent and consistent stories @xcite . in dialogue generation, narrative can be used to define a global plan for the whole conversation session, so as to avoid generating inconsistent figure 1 : an example of part of a script with a narrative extracted from our graphmovie dataset. the checked lines are from a ground-truth session, while the unchecked responses are other candidates that are relevant but not coherent with the narrative. and scattered responses @xcite @xcite . in this work, we investigate the utilization of narratives in a special case of text generationmovie script generation. this special form of conversation generation is chosen due to the unavailability of the data for a more general form of application. yet it does require the same care to leverage narratives in general conversation, and hence can provide useful insight to a more general form of narrative-guided conversation. the dataset we use to support our study is collected from graphmovie foot_0 , where an end-user retells the story of a movie by uploading descriptive paragraphs in his/her own words. more details about the dataset will be presented in section 3.2. an example is shown in figure 1 , where the narrative is uploaded to retell several lines of a script in a movie. our task is to generate/select the following lines by leveraging the narrative. our problem is closely related to dialogue generation that takes into account the context @xcite @xcite . however, a narrative plays a different and more specific role than a general context. in particular, a narrative may cover the whole story (a part of a script), thus a good conversation should also cover all the aspects mentioned in a narrative, which is not required with a general context. in this paper, we propose a new model called scriptwriter to address the problem of script generation/selection with the help of a narrative. scriptwriter keeps track of what in the narrative has been said and what is remaining to select the next line by an updating mechanism. the matching between updated narrative, context, and response are then computed respectively and finally aggregated as a matching score. as it is difficult to evaluate the quality of script generation, we frame our work in a more restricted case -selecting the right response among a set of candidates. this form of more limited conversation generationretrieval-based conversation -has been widely used in the previous studies @xcite , and it provides an easier way to evaluate the impact of narratives. we conduct experiments on a dataset we collected and made publicly available (see section 5). the experiments will show that using a narrative to guide the generation/selection of script is a much more appropriate approach than using it as part of the general context. our work has three main contributions: ## proposed method: scriptwriter 4.1 overview a good response is required to be coherent with the previous lines, i.e., context, and be consistent with the given narrative. for example, "just stay a little longer" can respond "mama's going to worry about me" and it has no conflict with the narrative in figure 1 . furthermore, as our target is to generate all lines in the session successively, it is also required that the following lines should convey the information that the former lines have not conveyed. otherwise, only a part of the narrative is covered, and we will miss some other aspects specified in the narrative. we propose an attention-based model called scriptwriter to solve the problem. scriptwriter follows a representation-matching-aggregation framework. first, the narrative, the context, and the response candidate are represented in multiple granularities by multi-level attentive blocks. second, we propose an updating mechanism to keep track of what in a narrative has been expressed and explicitly lower their weights in the updated narrative so that more emphasis can be put on the remaining parts. third, matching features are extracted between different elements: between context and response to capture whether it is a proper reply; between narrative and response to capture whether it is consistent with the narrative; and between context and narrative to implicitly track what in the narrative has been expressed in the previous lines. finally, the above matching features are concatenated together and a final matching score is produced by convolutional neural networks (cnns) and a multi-layer perceptron (mlp). ## conclusion and future work although story generation has been extensively studied in the literature, no existing work addressed the problem of generating movie scripts following a given storyline or narrative. in this paper, we addressed this problem in the context of generating dialogues in a movie script. we proposed a model that uses the narrative to guide the dialogue generation/retrieval. we keep track of what in the narrative has already been expressed and what is remaining to select the next line through an updating mechanism. the final selection of the next response is based on multiple matching criteria between context, narrative and response. we constructed a new large-scale data collection for narrative-guided script generation from movie scripts. this is the first public dataset available for testing narrativeguided dialogue generation/selection. experimental results on the dataset showed that our proposed approach based on narrative significantly outperforms the baselines that use a narrative as an additional context, and showed the importance of using the narrative in a proper manner. as a first investigation on the problem, our study has several limitations. for example, we have not considered the order in the narrative description, which could be helpful in generating dialogues in correct order. other methods to track the dialogue state and the coverage of narrative can also be designed. further investigations are thus required to fully understand how narratives can be effectively used in dialogue generation.