Identifying similar movie characters is a captivating task that can be our first step to understand the commonalities between human characteristics and experiences . Here, we seek to identify similar movie character descriptions and evaluate our findings based on whether they belong to a common fan-curated trope (theme). Rather than simply comparing the embedding representation of character description, we use a pair-wise attention model to make use of complex word/span-level relationships across the two character descriptions to predict the similarity of the two characters. Naively, such a model would require the exhaustive comparison of each character to all other characters, which is an O(n^2) operation with respect to the number of characters, making it unfeasible to be used in practice. We reduced this into an O(n) operation using a two-step approach that involves choosing only a tiny fraction of character-pairs to perform pairwise attention on while still being effective in this task. Our approach performs at least 9-27 \% better than methods based on state-of-the-art paragraph embedding representations. \\%DIF > 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.
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 \\%DIF > 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.
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Identifying similar movie characters is a captivating task that can be our first step to understand the commonalities between human characteristics and experiences . Here, we seek to identify similar movie character descriptions and evaluate our findings based on whether they belong to a common fan-curated trope (theme). Rather than simply comparing the embedding representation of character description, we use a pair-wise attention model to make use of complex word/span-level relationships across the two character descriptions to predict the similarity of the two characters. Naively, such a model would require the exhaustive comparison of each character to all other characters, which is an O(n^2) operation with respect to the number of characters, making it unfeasible to be used in practice. We reduced this into an O(n) operation using
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
R
involves choosing only a tiny fraction of character-pairs to perform pairwise attention on while still being effective in this task. Our approach performs at least
balances performance and efficiency, we managed to achieve
D
\% better than methods based on state-of-the-art paragraph embedding representations.