Exploring Neural and Prompt-Based Approaches for Event Detection in Short Stories

In literary narratives, events often transcend the boundaries of reality, encompassing a blend of real-world occurrences and imaginative constructs. This combination poses a unique challenge. Unlike domains like biomedical or news articles, events in literary narratives are not always based on facts. Distinguishing between these realis and non-realis events is crucial for a nuanced understanding of the narrative’s thematic underpinnings and the author’s stylistic choices. To address this challenge, we used the “Gatha-200” dataset, a collection of 200 short stories annotated for realis events. We employed a variety of neural models, including transformers, to detect realis events in the “Gatha-200” dataset. The transformer model achieved an impressive F1-score of 93.9%, showcasing its proficiency in identifying real-world events within literary contexts. Furthermore, we investigated the utility of prompt-based learning techniques for detecting realis events in zero-shot and few-shot scenarios. This approach proved to be effective, demonstrating the adaptability of our models to varying degrees of training data availability.