SMVAE

[Title] A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition

[Authors] Baohang Zhou, Ying Zhang, Kehui Song, Xuhui Sui, Guoqing Zhao, Hongbin Wang and Xiaojie Yuan

[EMNLP 2022 Main Conference]

Preparation

1. Clone the repo to your local.
2. Download Python version: 3.6.13
3. Download the dataset from this link and the extraction code is 1234. Put the downloaded files into the ''dataset'' folder.
4. Download the BERT pretrained models, and unzip into the ''pretrain'' folder.
5. Open the shell or cmd in this repo folder. Run this command to install necessary packages.

pip install -r requirements.txt

Experiments

1. For Linux systems, we have shell scripts to run the training procedures. You can run the following command:

./train.mvae.twitter2015.sh

or

./train.mvae.twitter2017.sh

2. You can also input the following command to train the model. There are different choices for some hyper-parameters shown in square barckets. The meaning of these parameters are shown in the following tables.

Parameters	Value	Description
epoch	int	Training times
patience	int	Early stopping
gamma	float	The percentage of
save_model	int	Whether to save the training model
latent_dim	int	Dimension number of latent variable
semi_supervised	int	Whether to run semi-supervised model
n_selected_samples	int	Number of labeled data samples
weights	string	Saved model path
dataset	string	Dataset name
mode	string	To train or test model

CUDA_VISIBLE_DEVICES=1 \
python train.mvae.py \
    --dataset twitter2017 \
    --seed 6 \
    --lr 1e-5 \
    --epoch 100 \
    --gamma 0.03 \
    --patience 20 \
    --save_model 1 \
    --latent_dim 100 \
    --semi_supervised 1 \
    --n_selected_samples 100 \
    --weights ./weights/twitter2017.model.h5 \
    --mode train \

When ''n_selected_samples'' parameter is set to -1, the all samples in ''train.txt'' will be used to train the model.

3. After training the model, you can change the ''mode'' parameter to ''test'' for evaluating the model on the test set.

4. We also provide the weights of the model to reimplement the results in our paper. You should download the weight files from this link and the extraction code is 1234, then put them into the ''weights'' folder. You can run the script directly after downloading the dataset and weights.

5. When you change the ''n_selected_samples'' parameter to run different experiments, you should delete the files except for ''train.txt, dev.txt, valid.txt, test.txt, images/'' in the ''./dataset/twitter2015/'' or ''./dataset/twitter2017/'' folders.