Controllable Multi-Character Psychology-Oriented Story Generation

• URL: http://arxiv.org/abs/2010.05230v1
• Date: Sun, 11 Oct 2020 12:05:00 GMT
• Title: Controllable Multi-Character Psychology-Oriented Story Generation
• Authors: Feifei Xu, Xinpeng Wang, Yunpu Ma, Volker Tresp, Yuyi Wang, Shanlin Zhou and Haizhou Du
• Abstract summary: We present a novel model-based attention mechanism that we call SoCP (Storytelling of multi-Character Psychology) We show that the proposed model can generate stories considering the changes in the psychological state of different characters. Experiments show that with SoCP, the generated stories follow the psychological state for each character according to both automatic and human evaluations.
• Score: 28.054245616281023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Story generation, which aims to generate a long and coherent story automatically based on the title or an input sentence, is an important research area in the field of natural language generation. There is relatively little work on story generation with appointed emotions. Most existing works focus on using only one specific emotion to control the generation of a whole story and ignore the emotional changes in the characters in the course of the story. In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters. To the best of our knowledge, this work is first to focuses on characters' emotional lines in story generation. We present a novel model-based attention mechanism that we call SoCP (Storytelling of multi-Character Psychology). We show that the proposed model can generate stories considering the changes in the psychological state of different characters. To take into account the particularity of the model, in addition to commonly used evaluation indicators(BLEU, ROUGE, etc.), we introduce the accuracy rate of psychological state control as a novel evaluation metric. The new indicator reflects the effect of the model on the psychological state control of story characters. Experiments show that with SoCP, the generated stories follow the psychological state for each character according to both automatic and human evaluations.

Related papers

• Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
  We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions. Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark. We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
  arXiv  Detail & Related papers  (2024-09-20T14:56:33Z)
• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• The Emotion Dynamics of Literary Novels [33.974558021764395]
  We use character dialogue to distinguish between the emotion arcs of the narration and the various characters. Our findings show that the narration and the dialogue largely express disparate emotions through the course of a novel.
  arXiv  Detail & Related papers  (2024-03-04T20:39:21Z)
• Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion [87.18073195745914]
  We investigate how well human-annotated emotion triggers correlate with features deemed salient in their prediction of emotions. Using EmoTrigger, we evaluate the ability of large language models to identify emotion triggers. Our analysis reveals that emotion triggers are largely not considered salient features for emotion prediction models, instead there is intricate interplay between various features and the task of emotion detection.
  arXiv  Detail & Related papers  (2023-11-16T06:20:13Z)
• Psychology-guided Controllable Story Generation [18.312272854479442]
  We introduce global psychological state chains, which include the needs and emotions of the protagonists, to help a story generation system create more controllable and well-planned stories. Psychiatric state trackers are employed to memorize the protagonist's local psychological states. psychological state planners are adopted to gain the protagonist's global psychological states for story planning.
  arXiv  Detail & Related papers  (2022-10-14T03:40:53Z)
• CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions [10.694612203803146]
  This paper proposes a model for fine-grained control on the story. It allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. It has strong controllability to generate stories according to the fine-grained personalized guidance.
  arXiv  Detail & Related papers  (2022-10-11T07:37:50Z)
• Speech Synthesis with Mixed Emotions [77.05097999561298]
  We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
  arXiv  Detail & Related papers  (2022-08-11T15:45:58Z)
• Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation [71.24817035071176]
  We propose a planning-based generation model named CONPER to explicitly model the relationship between personas and events. Both automatic and manual evaluation results demonstrate that CONPER outperforms state-of-the-art baselines for generating more coherent and persona-controllable stories.
  arXiv  Detail & Related papers  (2022-04-22T13:45:02Z)
• Computational Lens on Cognition: Study Of Autobiographical Versus Imagined Stories With Large-Scale Language Models [95.88620740809004]
  We study differences in the narrative flow of events in autobiographical versus imagined stories using GPT-3. We found that imagined stories have higher sequentiality than autobiographical stories. In comparison to imagined stories, autobiographical stories contain more concrete words and words related to the first person.
  arXiv  Detail & Related papers  (2022-01-07T20:10:47Z)
• Emotion Dynamics in Movie Dialogues [25.289525325790414]
  We introduce a framework to track emotion dynamics through one's utterances. We use this approach to trace emotional arcs of movie characters. We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories.
  arXiv  Detail & Related papers  (2021-03-01T23:02:16Z)
• Modeling Protagonist Emotions for Emotion-Aware Storytelling [16.09732485225391]
  We present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models.
  arXiv  Detail & Related papers  (2020-10-14T06:24:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.