MemN2N

Implementation of End-To-End Memory Networks with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset.

This is a fork of the original repository https://github.com/domluna/memn2n written by Dominique Luna.

Changes from the original implementation include -

1. L2 regularizations for weight matrices
2. Jaccard similarity for sentence selection to form memories
3. Per task early stopping during joint training
4. Web demo (see below)

Our results

Task	Testing Accuracy
1	99.6
2	67.4
3	57.6
4	98.4
5	83.1
6	99.3
7	85.8
8	91.8
9	99.3
10	95.7
11	97.5
12	99.2
13	98.3
14	87.7
15	100
16	48
17	61.3
18	92.1
19	10.8
20	100

Demo

We added a web demo allowing us to test the model and visualize the memory probabilities in each hop (episode). Below is an example that demonstrate it -