@inproceedings{tang-etal-2025-towards,
    title = "Towards Transferable Personality Representation Learning based on Triplet Comparisons and Its Applications",
    author = "Tang, Kai  and
      Wang, Rui  and
      Zhu, Renyu  and
      Lin, Minmin  and
      Ding, Xiao  and
      Lv, Tangjie  and
      Fan, Changjie  and
      Wu, Runze  and
      Wang, Haobo",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.509/",
    pages = "10061--10077",
    ISBN = "979-8-89176-332-6",
    abstract = "Personality is an important concept in psychology that reflects individual differences in thinking and behavior, and has significant applications across various fields. Most existing personality analysis methods address this issue at the bag level, treating the entire corpus gathered from one individual as a single unit for classification. However, this paradigm presents several challenges. From the data perspective, collecting a large corpus for each individual and performing comprehensive annotations pose significant difficulties in both data collection and labeling. On the application side, concentrating on classifying the entire corpus limits its applicability in more common single-instance scenarios. To address these issues, we propose a new task paradigm in text-based personality representation learning. Specifically, we construct a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties. This approach removes the traditional constraints on data sources, facilitating dataset expansion, and can leverage the transfer capabilities of embeddings to easily adapt to various downstream tasks. Our experiments show that the learned embeddings significantly boost performance by a relative 10{\%} across various applications, including personality detection, personality retrieval, and emotion translation prediction. The code and dataset are available at \url{https://github.com/zjutangk/PTCD}."
}