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.

提出了两种反映演员与角色关系的任务：演员选角预测和演员多才度排名，并提出了一种将演员、电影、角色、类型和描述性关键词嵌入为高斯分布和翻译向量的技术，表明自动识别的人物主题能够在这两个任务上显著提高演员模型的性能。