Exploring Text Recombination for Automatic Narrative Level Detection
Nils Reiter, Judith Sieker, Svenja Guhr, Evelyn Gius, Sina Zarrieß
Abstract
Automatizing the process of understanding the global narrative structure of long texts and stories is still a major challenge for state-of-the-art natural language understanding systems, particularly because annotated data is scarce and existing annotation workflows do not scale well to the annotation of complex narrative phenomena. In this work, we focus on the identification of narrative levels in texts corresponding to stories that are embedded in stories. Lacking sufficient pre-annotated training data, we explore a solution to deal with data scarcity that is common in machine learning: the automatic augmentation of an existing small data set of annotated samples with the help of data synthesis. We present a workflow for narrative level detection, that includes the operationalization of the task, a model, and a data augmentation protocol for automatically generating narrative texts annotated with breaks between narrative levels. Our experiments suggest that narrative levels in long text constitute a challenging phenomenon for state-of-the-art NLP models, but generating training data synthetically does improve the prediction results considerably.- Anthology ID:
- 2022.lrec-1.357
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 3346–3353
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.357
- DOI:
- Cite (ACL):
- Nils Reiter, Judith Sieker, Svenja Guhr, Evelyn Gius, and Sina Zarrieß. 2022. Exploring Text Recombination for Automatic Narrative Level Detection. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3346–3353, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Exploring Text Recombination for Automatic Narrative Level Detection (Reiter et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.357.pdf