G raph P lan: Story Generation by Planning with Event Graph (2021)

Abstract: Story generation is a task that aims to automatically generate a meaningful story. This task is challenging because it requires high-level understanding of the semantic meaning of sentences and causality of story events. Naivesequence-to-sequence models generally fail to acquire such knowledge, as it is difficult to guarantee logical correctness in a text generation model without strategic planning. In this study, we focus on planning a sequence of events assisted by event graphs and use the events to guide the generator. Rather than using a sequence-to-sequence model to output a sequence, as in some existing works, we propose to generate an event sequence by walking on an event graph. The event graphs are built automatically based on the corpus. To evaluate the proposed approach, we incorporate human participation, both in event planning and story generation. Based on the largescale human annotation results, our proposed approach has been shown to provide more logically correct event sequences and stories compared with previous approaches.

## introduction narrative intelligence @xcite is a form of humanistic artificial intelligence that requires the system to organize, comprehend, and reason about narratives, and then, produce meaningful responses. story generation tasks can be considered as a test bed for examining whether a system develops a good understanding of the narratives. in addition to leaving the model to output random sequences, the model is usually given a specific topic (e.g., title or prompt) or visual information (e.g., image or video). one straightforward approach for these story generation tasks is to leverage a sequence-to-sequence model to predict sentences sequentially. although the model can be trained to capture the word-prediction dis-figure 1 : comparison between sequence-to-sequence model and graphplan (ours). two problems occur in the sequence-to-sequence model when generating events: repetition and logical inconsistency. repeated words (e.g., play) in the storyline result in repeated sentences in the generated stories. in addition, the logic between "land" and "snap" lacks causality, thus generating incoherent stories. on the contrary, our graph-plan method does not rely on any language model, and applies beam search on the event graph based on a well-designed score function. the mutually exclusive set further ensures global logical consistency for the planned events. recently, there has been significant interest in decomposing story generation into two phases: planning and generation @xcite @xcite . planning @xcite creates a high-level abstraction or a blueprint that encourages the generator to focus on the flow of a story, similar to making an outline before writing. the planned elements are referred to as events in several papers. however, the detailed definition of events varies. for instance, an event can be represented as a verb argument pair (e.g., (admits, subj)) @xcite : tuple of subject, verb, object and modifier or "wildcard" (e.g., (person0, correspond-36.1, empty, person1)) @xcite or reconstructed verb phrase (e.g., decide(go)) @xcite . in this paper, we follow @xcite to represent an event with verb phrases. existing approaches @xcite @xcite regard event generation as an abstracted case of story generation. in other words, they treat each event as one token and use a sequence-to-sequence model to plan the events. our preliminary experiments show that repetition and logical inconsistency problems occur in the event sequence; further, the same problems occur in the generated stories. figure 1 shows an example using sequence-tosequence event planning. both events and stories are repeated and illogical. in this study, instead of a sequence-to-sequence model for event planning, we propose a planning method, graphplan. to plan the event, graphplan walks on a topic-specific event graph with a beam search. event graphs have been adopted for story generation even before the emergence of neuralbased models @xcite @xcite @xcite . an event graph represents the logical flow of events based on the facts presented in a corpus. we can walk on a learned event graph and produce a reasonable event sequence. we follow the graph setting in @xcite , wherein each graph is composed of event nodes, functions and a set of mutually exclusive events. to generate a story, we first identify the topic based on the input (e.g., title or image), and subsequently, retrieve a related event graph. we then plan the events by running a beam search with a score function that considers event-event coherence and input-event coherence into account. finally, a story generation module transforms the planned event sequence into a readable story. figure 2 depicts the entire pipeline of our proposed approach. we conducted experiments on open story generation to evaluate how event graphs benefit tasks. our approach significantly outperforms baseline models that generate events with sequence-to-sequence models in terms of logical consistency. we also conducted story cloze test (sct) to further validate the effectiveness of the event graphs and the mutually exclusive sets. our contributions can be summarized as follows: ## event graph construction as a preprocessing step, we first extract events automatically from a corpus. then, we divide the corpus into several topics. finally, we build an event graph for each topic. ## graphplan: planned story generation using event graph in this section, we describe our approach for planned story generation. we separated the entire pipeline into two steps: 1) our graphplan walks on the event graph and produces a sequence of events as a blueprint of the story. ## discussion as mentioned before, the stories can be easily controlled by modifying the events. ## conclusion in this study, we demonstrate that a graph-based event planning approach can indeed produce more natural event sequences compared with conventional language models. we propose to walk on automatically learned event graphs by performing beam search using a score function dedicated for event planning. then, the story is generated follows the planned events. we evaluate our approach on event planning and open story generation with large-scale human judgements. the results show that our proposed approach clearly outperforms the non-planning baseline and the sequence-to-sequence model-based planning models. in human evaluation, the events and the stories generated by our proposed method are believed to be more logical and coherent. an additional experiment on story cloze test further proves the advantages of event graphs and mutually exclusive sets.