Key Value Memory Networks

This repo contains the implementation of Key Value Memory Networks for Directly Reading Documents in Tensorflow. The model is tested on bAbI.

Tutorial

There is a must-read tutorial on Memory Networks for NLP from Jason Weston @ ICML 2016.
[Video] [Slides] Sumit Chopra, from Facebook AI, gave a lecture about Reasoning, Attention and Memory at Deep Learning Summer School 2016.

Get Started

git clone https://github.com/siyuanzhao/key-value-memory-networks.git

mkdir ./key-value-memory-networks/logs
mkdir ./key-value-memory-networks/data/
cd ./key-value-memory-networks/data
wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz
tar xzvf ./tasks_1-20_v1-2.tar.gz

cd ../
python single.py

Usage

Running a single bAbI task

# Train the model on a single task <task_id>
python single.py --task_id <task_id>

There are serval flags within single.py. Below is an example of training the model on task 20 with specific learning rate, feature_size and epochs.

python single.py --task_id 20 --learning_rate 0.005 --feature_size 40 --epochs 200

Check all avaiable flags with the following command.

python single.py -h

Running a joint model on all bAbI tasks

python joint.py

There are also serval flags within joint.py. Below is an example of training the joint model with specific learning rate, feature_size and epochs.

python joint.py --learning_rate 0.005 --feature_size 40 --epochs 200

Check all avaiable flags with the following command.

python joint.py -h

Results

Bag of Words

Joint Model

The model is jointly trained on 20 tasks (1k training examples / weakly supervised) with following hyperparameters.

BATCH_SIZE=50
EMBEDDING_SIZE=40
EPOCHS=200
EPSILON=0.1
FEATURE_SIZE=50
HOPS=3
L2_LAMBDA=0.1
LEARNING_RATE=0.001
MAX_GRAD_NORM=20.0
MEMORY_SIZE=50
READER=bow

python joint.py

Task	Testing Accuracy	Training Accuracy	Validation Accuracy
1	1.00	1.00	1.00
2	0.80	0.87	0.85
3	0.66	0.77	0.69
4	0.73	0.79	0.74
5	0.84	0.91	0.80
6	0.98	0.99	0.98
7	0.83	0.85	0.80
8	0.89	0.92	0.86
9	0.98	0.99	0.96
10	0.85	0.96	0.89
11	0.97	0.98	0.99
12	0.99	0.99	1.00
13	0.99	0.99	1.00
14	0.80	0.90	0.84
15	0.56	0.57	0.45
16	0.46	0.48	0.37
17	0.57	0.72	0.70
18	0.93	0.95	0.92
19	0.10	0.11	0.06
20	0.98	0.99	0.99

results on 10k training examples are here

RNN(GRU)

Requirements

tensorflow 1.10
scikit-learn 0.19
six 1.10.0

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