David-Li0406/Script-based-Character-Understanding

Multi-level Contrastive Learning for Script-based Character Understanding

Overview

Dependency

To run our multi-level contrastive learning framework in each individual task, please first install the required packages from the original repositories here and here.

Data

You can download the processed data here directly.

For character linking and coreference resolution, put the files in original_data into character linking & coreference_resolution/data, and put the files in data into character linking & coreference_resolution/joint_model/data.

For character guessing, put the files in dataset into character guessing/dataset.

Run

Character Linking & Coreference Resolution

To get C2 result in character linking and coreference resolution, run

cd character linking & coreference resolution/joint_model
bash train.sh

Character Guessing

TBA

Results with LLMs

We also include the code to inference each dataset with ChatGPT. You will first need to add you OpenAI API key into inference_api.py.

For character linking and coreference resolution, run

cd character linking & coreference resolution/joint_model
python inference_api.py

For character guessing, run

cd character guessing
python inference_api.py

Credits: This work began as a fork of the C2 and TVSHOWGUESS repository. If you found our code useful, please consider citing:

@inproceedings{li-etal-2023-multi-level,
    title = "Multi-level Contrastive Learning for Script-based Character Understanding",
    author = "Li, Dawei  and
      Zhang, Hengyuan  and
      Li, Yanran  and
      Yang, Shiping",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.366",
    doi = "10.18653/v1/2023.emnlp-main.366",
    pages = "5995--6013",
    abstract = "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 in this URL.",
}