This is the tensorflow version implementation/reproduce of some reading comprehension models in some reading comprehension datasets including the following:
Models:
- Attention Sum Reader model as presented in "Text Comprehension with the Attention Sum Reader Network" (ACL2016) available at http://arxiv.org/abs/1603.01547.
- Attention over Attention Reader model as presented in "Attention-over-Attention Neural Networks for Reading Comprehension" (arXiv2016.7) available at https://arxiv.org/abs/1607.04423.
Datasets:
- CBT, Children’s Book Test.http://lanl.arxiv.org/pdf/1506.03340.pdf
git clone https://github.com/zhanghaoyu1993/RC-experiments.git- Download and extract the dataset used in this repo.
cd data
./prepare-all.sh- Python-64bit >= v3.5.
- Install require libraries using the following command.
pip install -r requirements.txt- Install tensorflow >= 1.1.0.
pip install tensorflow-gpu --upgrade- Install nltk punkt for tokenizer.
python -m nltk.downloader punkt- What is the entrance of the program?
The main.py file in root directory.
- How can I specify a model in command line?
Type a command like above, the model_class is the class name of model, usually named in cambak-style:
python main.py [model_class]For example, if you want to use AttentionSumReader:
python main.py AttentionSumReader- How can I specify the dataset?
Type a command like above, the dataset_class is the class name of dataset:
python main.py [model_class] --dataset [dataset_class]For example, if you want to use CBT:
python main.py [model_class] --dataset CBTYou don't need to specify the data_root and train valid test file name in most cases, just specify the dataset.
- How can I know all the parameters?
The program use argparse to deal with parameters, you can type the following command to get help:
python main.py --helpor:
python main.py -h- The command parameters is so long!
The parameters will be stored into a file named args.json when executed, so next time you can type the following simplified command:
python main.py [model_class] --args_file [args.json]First, modify the parameters in the args.json.
You can now train and test the model by entering the following commands. The params in [] should be determined by the real situation.
- Train:
python main.py [model_class] --args_file [args.json] --train 1 --test 0 After train, the parameters are stored in weight_path/args.json and the model checkpoints are stored in weight_path.
- Test:
python main.py [model_class] --args_file [args.json] --train 0 --test 1 After test, the performance of model are stored in weight_path/result.json.
All the trained results and corresponding config params are saved in sub directories of weight_path(by default the weight folder) named args.json and result.json.
You should know that the implementation of some models are slightly different from the original, but the basic ideas are same, so the results are for reference only.
The best results of implemented models are listed below:
- best result we achieve(with little hyper-parameter tune in single model)
- best result listed in original paper(in the brackets)
| CBT-NE | CBT-CN | |
|---|---|---|
| AS-Reader | 69.88(68.6) | 65.0(63.4) |
| AoA-Reader | 71.0(72.0) | 68.12(69.4) |
- How do I use args_file argument in the shell?
Once you enter a command in the shell(maybe a long one), the config will be stored in weight_path/args.json where weight_path is defined by another argument, after the command execute you can use --args.json to simplify the following command:
python main.py [model_class] --args_file [args.json]And the priorities of arguments typed in the command line is higher than those stored in args.json, so you can change some arguments.