Understanding Actors and Evaluating Personae with Gaussian Embeddings


  • Hannah Kim Georgia Institute of Technology
  • Denys Katerenchuk The City University of New York
  • Daniel Billet Independent Actor
  • Jun Huan Baidu Research
  • Haesun Park Georgia Institute of Technology
  • Boyang Li Baidu Research




Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors’ versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O’Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.




How to Cite

Kim, H., Katerenchuk, D., Billet, D., Huan, J., Park, H., & Li, B. (2019). Understanding Actors and Evaluating Personae with Gaussian Embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6570-6577. https://doi.org/10.1609/aaai.v33i01.33016570



AAAI Technical Track: Natural Language Processing