Papers by I-Ta Lee
Modeling Human Mental States with an Entity-based Narrative Graph (2021.naacl-main)
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| Challenge: | Understanding narrative text requires capturing characters’ motivations, goals, and mental states. |
| Approach: | They propose an Entity-based Narrative Graph (ENG) to model the internal-states of characters in a story and evaluate it on two narrative understanding tasks. |
| Outcome: | The proposed model is based on two narrative understanding tasks: predicting character mental states, and desire fulfillment. |
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)
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| Challenge: | Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. |
| Approach: | They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. |
| Outcome: | The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation. |
Multi-Relational Script Learning for Discourse Relations (P19-1)
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| Challenge: | Existing script knowledge models only represent a single event relationship, co-occurrence . this is coarse for commonsense, which should account for fine-grained relationships . |
| Approach: | They propose to view learning event embedding as a multi-relational problem . they model a rich set of event relations derived from the Penn Discourse Tree Bank . |
| Outcome: | The proposed model captures different aspects of event pairs, including cause and contrast. |
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (2020.findings-emnlp)
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| Challenge: | Structured knowledge representations capture temporal relations between events to describe human-level representations of common scenarios. |
| Approach: | They propose to represent narrative graphs and learn contextualized event representations over them using a relational graph neural network model. |
| Outcome: | The proposed model improves performance when learning script knowledge without supervision and provides a better representation for the implicit discourse sense classification task. |