Query-Reduction Networks (QRN)

QRN is a purely sequential model like LSTM or GRU (but simpler than them) for story-based question answering (bAbI QA tasks). QRN is implemented using TensorFlow. Here are some notable results (error rates in %) on bAbI QA dataset:

Task	LSTM	MemN2N	Ours
1k avg	51.3	15.2	9.9
10k avg	36.4	4.2	0.3

See model details and more results in this paper.

1. Quick Start

We are assuming you are working in a Linux environment. Make sure that you have Python (verified on 3.5, issues have been reported with 2.x), and you installed these Python packages: tensorflow (>=0.8, <=0.11, issues have been reported with >=0.12) and progressbar2.

First, download bAbI QA dataset (note that this downloads the dataset to $HOME/data/babi):

chmod +x download.sh; ./download.sh 

Then preprocess the data for a particular task, say Task 2 (this stores the preprocessed data in data/babi/en/02/):

python -m prepro --task 2

Finally, you train the model (test is automatically performed at the end):

python -m babi.main --noload --task 2

It took ~3 minutes on my laptop using CPU.

You can run it several times with new weight initialization (e.g. 10) and report the test result with the lowest dev loss:

python -m babi.main --noload --task 2 --num_trials 10

This is critical to stably get the reported results; some weight initialization leads to a bad optima.

2. Visualizing Results

After training and testing, the result is stored in evals/babi/en/02-None-00-01/test_0150.json. We can visualize the magnitudes of the update and reset gates using the result file. Note that you need jinja2 (Python package). Run the following command to host a web server for visualization and open it via browser:

python -m babi.visualize_result --task 2 --open True

then click the file(s). It takes a a few seconds to load the heatmap coloring of the gate values. You will see something like this:

By default visualize_result retrieves the first trial (1). If you want to retrieve a particular trial number, specify the trial number if --trial_num option.

3. 10k and Other Options

To train the model on 10k dataset, first preprocess the data with large flag:

python -m prepro --task 2 --large True

Then train the model with large flag as well:

python -m babi.main --noload --task 2 --large True --batch_size 128 --init_lr 0.1 --wd 0.0005 --hidden_size 200

Note that the batch size, init_lr, wd, and hidden_size changed.

Finally, visualization requires the large flag:

python -m babi.visualize_result --task 2 --open True --large True

To control other parameters and see other options, type:

python -m babi.main -h

4. Run bAbI dialog

To train the model on bAbI dialog, preprocess the data with bAbI dialog dataset:

python -m prepro-dialog --task 2

Then train the model:

python -m dialog.main --noload --task 2

To use match, use_match flag is required:

python -m dialog.main --noload --task 2 --use_match True

To use RNN decoder, use_rnn flag is required:

python -m dialog.main --noload --task 2 --use_rnn True