PromptEM is a novel low-resource GEM (Generalized Entity Matching) solution powered by prompt-tuning and self-training. To address the gap between pre-training and fine-tuning, we cast GEM as a cloze-style task and design the GEM-specific prompt-tuning, which can stimulate the rich knowledge distributed in LMs. To select high-quality pseudo-labels, we develop a lightweight uncertainty-aware self-training method to boost performance. To further avoid expensive self-training, we prune useless training data dynamically using the proposed MC-EL2N, making the self-training process more lightweight and efficient.
For more technical details, see PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching.
We use eight real-world benchmark datasets with different structures from Machamp and Geo-ER.
To train and evaluate with PromptEM.
python main.py [<args>] [-h | --help]
e.g.
python main.py -d=rel-heter -k=0.1 -st -dd=8 -ur=0.05 -er=0.05
The meaning of the flags:
--model_name_or_path: the name or local path of the pre-trained language model. e.g.roberta-base--data_name: the name of the dataset. options:[rel-heter, rel-text, semi-heter, semi-homo, semi-rel, semi-text-c,semi-text-w, geo-heter, all]--k: the proportion of training data used. e.g.0.1--num_iter: the number of iterations. e.g.1--template_no: the number of templates used in PromptEM. options:[0,1,2,3]--self_training: the flag to enable self-training of PromptEM.--dynamic_dataset: the frequency of dynamic dataset pruning. e.g.8(pruning for every 8 epochs)--pseudo_label_method: the method of generating pseudo labels. e.g.uncertainty--mc_dropout_pass: the number of MC-Dropout passes. e.g.10--uncertainty_ratio: the proportion of the unlabeled samples for generating pseudo labels. e.g.0.05--el2n_ratio: the proportion of the labeled samples for dynamic dataset pruning. e.g.0.1--text_summarize: the flag to enable text summarization in entity serialization.--add_token: the flag to add special token in entity serialization.--max_length: the maximum length (in number of tokens) for the inputs to the transformer model. e.g.512--teacher_epochs: the number of epochs for training teacher model. e.g.20--student_epochs: the number of epochs for training student model. e.g.30--batch_size: batch size. e.g.32--lr: learning rate. e.g.1e-5
All the experiments are conducted on an Ubuntu Server with an Intel Xeon Silver 4216 CPU and an NVIDIA A100 GPU.
conda create -n promptem python=3.7
const activate promptem
pip install torch==1.10.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==4.16.2
pip install scikit-learn==1.0.2
Note that you do not need to install OpenPrompt by pip manually.
We notice that the best hyper-parameters can be sensitive to your server environment and package version. If you do not have the same environment, we highly recommend you run the search for hyper-parameters in your environment.
We provide an example search script in search.sh
We use RoBERTa-base as the backbone structure of our model in all the experiments.
You can download the pre-trained checkpoint from huggingface manually.
You can train the model using the best hyper-parameters we provided in low_configs.json.
We also provide the corresponding logs in logs.
See Quick Start for more details of training parameters.
