"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
This work addresses the challenge of enabling machines to understand characters in narratives, which is incremental as it provides a new dataset and tasks for research in this domain.
The authors tackled the problem of character-centric narrative understanding by introducing LiSCU, a dataset of literary pieces with character descriptions, and two new tasks: Character Identification and Character Description Generation. Experiments with pre-trained language models showed that current models are insufficient for these tasks, indicating a need for improved narrative comprehension models.
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, 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.