OOV MemN2N With Introspection

Implementation of End-To-End Memory Networks using Tensorflow. Tasks are from the bAbl dataset and relative OOV Tasks

We propose the OOV MemN2N With Introspection compared with the [End-To-End Memory Networks].

Get Started

git clone git@github.com:domluna/memn2n.git

mkdir ./memn2n/data/
cd ./memn2n/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

Examples

Running a single bAbI task

These files are also a good example of usage.

Requirements

• tensorflow 1.4+

Single Task Results

For a task to pass it has to meet 95%+ testing accuracy. Measured on single tasks on the 1k data.

Pass: 1,4,12,15,20

Several other tasks have 80%+ testing accuracy.

Stochastic gradient descent optimizer was used with an annealed learning rate schedule as specified in Section 4.2 of End-To-End Memory Networks

The following params were used:

• epochs: 100
• hops: 3
• embedding_size: 20

Task	Training Accuracy	Validation Accuracy	Testing Accuracy
1	1.0	1.0	1.0
2	1.0	0.86	0.83
3	1.0	0.64	0.54
4	1.0	0.99	0.98
5	1.0	0.94	0.87
6	1.0	0.97	0.92
7	1.0	0.89	0.84
8	1.0	0.93	0.86
9	1.0	0.86	0.90
10	1.0	0.80	0.78
11	1.0	0.92	0.84
12	1.0	1.0	1.0
13	0.99	0.94	0.90
14	1.0	0.97	0.93
15	1.0	1.0	1.0
16	0.81	0.47	0.44
17	0.76	0.65	0.52
18	0.97	0.96	0.88
19	0.40	0.17	0.13
20	1.0	1.0	1.0

OOV Testing Accuracy is aviable on our Paper.

Notes

Single task results are from 10 repeated trails of the single task model accross all 20 tasks with different random initializations. The performance of the model with the lowest validation accuracy for each task is shown in the table above.