In this work, we tackle the scenario of understanding characters in scripts, which aims to learn the characters' personalities and identities from their utterances. We begin by analyzing several challenges in this scenario, and then propose a multi-level contrastive learning framework to capture characters' global information in a fine-grained manner. To validate the proposed framework, we conduct extensive experiments on three character understanding sub-tasks by comparing with strong pre-trained language models, including SpanBERT, Longformer, BigBird and ChatGPT-3.5. Experimental results demonstrate that our method improves the performances by a considerable margin. Through further in-depth analysis, we show the effectiveness of our method in addressing the challenges and provide more hints on the scenario of character understanding. We will open-source our work on github at https://github.com/David-Li0406/Script-based-Character-Understanding.

翻译：在本工作中，我们探讨了剧本中角色理解的场景，旨在从角色的对话中学习其性格与身份。我们首先分析了该场景中的若干挑战，随后提出了一种多层次对比学习框架，以细粒度方式捕捉角色的全局信息。为验证所提出框架的有效性，我们在三个角色理解子任务上进行了广泛实验，并与包括SpanBERT、Longformer、BigBird和ChatGPT-3.5在内的强预训练语言模型进行了对比。实验结果表明，我们的方法在性能上取得了显著提升。通过进一步深入分析，我们展示了本方法在应对挑战方面的有效性，并为角色理解场景提供了更多启示。我们将在GitHub上开源相关工作，地址为https://github.com/David-Li0406/Script-based-Character-Understanding。