Regularization of Distinct Strategies for Unsupervised Question Generation
We propose a novel regularization method for avoiding a bias toward a particular question generation strategy and modulating the process of generating individual words when a question is generated.
This repo provides codes for BERT-QA (by Hugginface), BERT-QG (Two assistants and Student), and Discriminator.
Installation & Dependency
-
We use transformers from HuggingFace for BERT.
pip install transformers pip install torch==1.4 pip install packaging pip install tensorboardX pip install boto3
Pre-trained models
- Download the trained models. This includes copy_type_assistant, lm_type_assistant, student, discriminator, and bert-base-uncased (for BERTQG).
-
Once you have downloaded the models, you should move them as follows (We assume your current directory is in UQA):
unzip all_models.zip mkdir models cp -r all_models/discriminator.bin models/ cp -r all_models/* BERTQG/models/ rm -rf all_models
Data
We provide the data generated by our models, and used for training our models.
-
Once you download the data, unzip data.zip and you will get a directory called
data.unzip data.zip
Inside the automatically created data folder, you will find the following files:
QA_train_10k.json: a training set for QA by generated our student model (final model).student_gen_10k.json: a dataset, consists of (context, answer), to be used for generation of questions by the student.student_train_10k.json: a training set for the student by generated our teacher (soft target).teacher_gen_10k.json: a dataset, consists of (context, answer), to be used for generation of questions by the teacher (i.e., two assistants + regularization module).lm_type_train_10k.json / copy_type_train_10k.json: a training set for the assistants.Disc_lm-type_40k.json / Disc_copy-type_40k.json: a dataset used for training and evaluation for the discriminator.
Make sure that these datasets are in data/ to execute the following scripts.
Usage of scripts
-
BERTQA/run_squad.py: a script for testing the generated questions on a QA model (BERT-QA from HuggingFace). Note that we set dropout to 0, and use sequential sampling for discriminative results. With this setting, you should get a F1 score of about 60 on SQuAD 1.1 as in the paper.wget -P data https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json python BERTQA/run_squad.py \ --model_type bert \ --model_name_or_path bert-large-uncased-whole-word-masking \ --do_train \ --do_eval \ --do_lower_case \ --train_file data/QA_train_10k.json \ --predict_file data/dev-v1.1.json \ --per_gpu_train_batch_size 8 \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir BERTQA/models/ \ --logging_steps 5000 \ --save_steps 5000 \ -
student_generation.py: a script for generating a QA training set using trained student model (final model).python3 student_generation.py \ --bert_model BERTQG/models/bert-base-uncased \ --student BERTQG/models/student/pytorch_model.bin \ --input_file data/student_gen_10k.json \ --output_file data/QA_train_10k.json \ -
student_training.py: a script for training the student model with the training set (soft target) generated by teacher.python3 student_training.py \ --bert_model BERTQG/models/bert-base-uncased \ --do_train \ --train_file data/student_train_10k.json \ --output_dir BERTQG/models/student/ \ --num_train_epochs 2 \ --train_batch_size 6 \ --max_seq_length 512 \ --doc_stride 450 \ --max_query_length 42 \ -
teacher_generation.py: a script for generating the training set (soft target) using the trained teacher model. You may directly use the data generated by the teacher for QA as a regularization without student (more details in the paper).python3 teacher_generation.py \ --bert_model BERTQG/models/bert-base-uncased \ --lm_qg BERTQG/models/lm_type_assistant/pytorch_model.bin \ --uqa_qg BERTQG/models/copy_type_assistant/pytorch_model.bin \ --regul_model models/discriminator.bin \ --input_file data/teacher_gen_10k.json \ --output_file data/student_train_10k.json \ -
assistant_training.py: a script for training assistants. If the dataset is copy-type, the trained QG would be the copy-type assisant. If, on the other hand, the dataset is is LM-type, then the trained QG would be the LM-type assistant.python3 assistant_training.py \ --bert_model BERTQG/models/bert-base-uncased \ --do_train \ --train_file data/lm_type_train_10k.json \ --output_dir models/lm_type_assistant/ \ --num_train_epochs 2 \ --train_batch_size 6 \ --max_seq_length 512 \ --doc_stride 450 \ --max_query_length 42 \ -
Discriminator_training.py: a script for training the discriminator.python3 Discriminator_training.py