Papers with single-sentence personality embeddings

1 papers
Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications (2025.emnlp-main)

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Challenge: Existing methods for personality analysis treat corpus as a single unit for classification, but this approach presents several challenges.
Approach: They propose a task paradigm for text-based personality representation learning that uses a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties.
Outcome: The proposed model significantly boosts performance across various applications, including personality detection, personality retrieval, and emotion translation prediction.

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