The source code of AAAI2023 paper "Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection". The document describes how to reproduce the experiment result. We also provide a Chinese version document.
Datasets and post-trained BERT are available in Google Drive and Baidu Netdisk (verification code: 96ap). Preprocessing files are provided in folders pandora and kaggle, respectively.
There are 2 datasets, Kaggle and Pandora MBTI datasets for our evaluations.
Example cmd for running experiments on Kaggle
python main.py \
--all_gpu_train_batch_size 8 \
--all_gpu_eval_batch_size 32 \
--num_train_epochs 25.0 \
--task kaggle \
--l0
apply --l0 to enable L0 constraint on L2C, corresponding to Eq(15) in the paper, otherwise the training objective function is cross-entroty loss.
all_gpu_train_batch_size is set to 8 for Kaggle, and 4 for Pandora.
Best checkpoint is available in Google Drive.
Reproduing some ablation experiments need to modify source code, we will improve it in the future work.