Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences
This work addresses the challenge of modeling complex human experiences in NLP, though it appears incremental in its methodological approach.
The paper tackles the problem of capturing theme-level similarities between movie characters using a two-step approach, achieving 9-27% improvement over paragraph-embedding methods. It demonstrates potential applications for understanding human experiences in Reddit posts.
While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness. We propose a new task to capture this richness based on an unlikely setting: movie characters. We sought to capture theme-level similarities between movie characters that were community-curated into 20,000 themes. By introducing a two-step approach that balances performance and efficiency, we managed to achieve 9-27\% improvement over recent paragraph-embedding based methods. Finally, we demonstrate how the thematic information learnt from movie characters can potentially be used to understand themes in the experience of people, as indicated on Reddit posts.