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.
@article{arxiv.2310.13231,
title = {Multi-level Contrastive Learning for Script-based Character Understanding},
author = {Dawei Li and Hengyuan Zhang and Yanran Li and Shiping Yang},
journal= {arXiv preprint arXiv:2310.13231},
year = {2023}
}
Comments
Accepted by EMNLP 2023 main conference; Camera-ready version will be updated soon