Grounding Characters and Places in Narrative Texts

• URL: http://arxiv.org/abs/2305.17561v1
• Date: Sat, 27 May 2023 19:31:41 GMT
• Title: Grounding Characters and Places in Narrative Texts
• Authors: Sandeep Soni, Amanpreet Sihra, Elizabeth F. Evans, Matthew Wilkens, David Bamman
• Abstract summary: We propose a new spatial relationship categorization task. The objective of the task is to assign a spatial relationship category for every character and location co-mention within a window of text. We train a model using contextual embeddings as features to predict these relationships.
• Score: 5.254909030032427
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Tracking characters and locations throughout a story can help improve the understanding of its plot structure. Prior research has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. Here, we address this gap by proposing a new spatial relationship categorization task. The objective of the task is to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope. To this end, we annotate spatial relationships in approximately 2500 book excerpts and train a model using contextual embeddings as features to predict these relationships. When applied to a set of books, this model allows us to test several hypotheses on mobility and domestic space, revealing that protagonists are more mobile than non-central characters and that women as characters tend to occupy more interior space than men. Overall, our work is the first step towards joint modeling and analysis of characters and places in narrative text.

Related papers

• CHIRON: Rich Character Representations in Long-Form Narratives [98.273323001781]
  We propose CHIRON, a new character sheet' based representation that organizes and filters textual information about characters. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.
  arXiv  Detail & Related papers  (2024-06-14T17:23:57Z)
• Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives [21.297972871264744]
  We introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically, we designed hierarchical relationship categories and manually extracted and annotated role-oriented relationships from the perspectives of various characters. Our experiments with advanced Large Language Models (LLMs) like GPT-3.5, GPT-4, and Llama2 reveal their limitations in inferencing complex relationships and handling longer narratives.
  arXiv  Detail & Related papers  (2024-02-16T19:59:45Z)
• Detecting and Grounding Important Characters in Visual Stories [18.870236356616907]
  We introduce the VIST-Character dataset, which provides rich character-centric annotations. Based on this dataset, we propose two new tasks: important character detection and character grounding in visual stories. We develop simple, unsupervised models based on distributional similarity and pre-trained vision-and-language models.
  arXiv  Detail & Related papers  (2023-03-30T18:24:06Z)
• M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations [14.64546899992196]
  We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. Our model is able to achieve significant improvements in the task of identifying climax and resolution.
  arXiv  Detail & Related papers  (2023-02-18T20:48:02Z)
• Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization [81.26077816854449]
  We first explore the use of constituency parse trees for encoding structured input. Second, we augment the structured input with commonsense information and study the impact of this external knowledge on the generation of visual story. Third, we incorporate visual structure via bounding boxes and dense captioning to provide feedback about the characters/objects in generated images.
  arXiv  Detail & Related papers  (2021-10-21T00:16:02Z)
• "Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding [31.803481510886378]
  We present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
  arXiv  Detail & Related papers  (2021-09-12T06:12:55Z)
• Telling Stories through Multi-User Dialogue by Modeling Character Relations [14.117921448623342]
  This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.
  arXiv  Detail & Related papers  (2021-05-31T15:39:41Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Spatially Aware Multimodal Transformers for TextVQA [61.01618988620582]
  We study the TextVQA task, i.e., reasoning about text in images to answer a question. Existing approaches are limited in their use of spatial relations. We propose a novel spatially aware self-attention layer.
  arXiv  Detail & Related papers  (2020-07-23T17:20:55Z)
• Understanding Spatial Relations through Multiple Modalities [78.07328342973611]
  spatial relations between objects can either be explicit -- expressed as spatial prepositions, or implicit -- expressed by spatial verbs such as moving, walking, shifting, etc. We introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings.
  arXiv  Detail & Related papers  (2020-07-19T01:35:08Z)
• PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking [128.76063992147016]
  We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states. In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative.
  arXiv  Detail & Related papers  (2020-04-30T17:16:31Z)

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.