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# ADNC
# Advanced Differentiable Neural Computer
[![Build Status](https://travis-ci.org/joergfranke/ADNC.svg?branch=master)](https://travis-ci.org/joergfranke/ADNC)
[![Python](https://img.shields.io/badge/python-3.6-yellow.svg)](https://www.python.org/downloads/release/python-365/)
[![Python](https://img.shields.io/badge/python-3.5+-yellow.svg)](https://www.python.org/downloads/release/python-365/)
[![TensorFLow](https://img.shields.io/badge/TensorFlow-1.8-yellow.svg)](https://www.tensorflow.org/)
*THIS REPOSITORY IS IN CONSTRUCTION, NOT EVERYTHING IS WORKING FINE YET*
A Differentiable Neural Computer (DNC) implementation with advancements for a more robust and scalable usage. It
contains applications to the [20 bAbI QA tasks](https://research.fb.com/downloads/babi/) and the [CNN Reading
Comprehension Task](https://github.com/danqi/rc-cnn-dailymail).
This repository contains a implementation of a Differentiable Neural Computer (DNC) with advancements for a more robust and
scalable usage in Question Answering. It is applied to:
_This repository is based on a private repository and will be complete within June 2018._
- [20 bAbI QA tasks](https://research.fb.com/downloads/babi/) with [state-of-the-art results](#babi-results)
- [CNN Reading Comprehension Task](https://github.com/danqi/rc-cnn-dailymail) with
passable results without any adaptation.
This repository is the groundwork for the MRQA 2018
paper submission "Robust and Scalable Differentiable Neural Computer for Question Answering". It contains a modular and
fully configurable DNC with the following advancements:
<table>
<tbody>
<tr>
<td align="center"><img src="images/dnc_bd.png" alt="drawing" width="140px"/></td>
<td align="center"><img src="images/dnc_ln.png" alt="drawing" width="130px"/></td>
<td align="center"><img src="images/cbmu.png" alt="drawing" width="250px"/></td>
<td align="center"><img src="images/bidnc.png" alt="drawing" width="250px"/></td>
</tr>
<tr>
<td align="center"><b>Bypass Dropout </b></td>
<td align="center"><b>DNC Normalization</b></td>
<td align="center"><b>Content Based Memory Unit</b></td>
<td align="center"><b>Bidirectional Controller</b></td>
</tr>
<tr>
<td>
<ul>
<li>Dropout to reduce the bypass connectivity</li>
<li>Forces an earlier memory usage during training</li>
</ul>
</td>
<td>
<ul>
<li>Normalizes the memory unit's input %like layer normalization</li>
<li>Increases the model stability during training</li>
</ul>
</td>
<td>
<ul>
<li>Memory Unit without temporal linkage mechanism</li>
<li>Reduces memory consumption by up to 70</li>
</ul>
</td>
<td>
<ul>
<li>Bidirectional DNC Architecture</li>
<li>Allows to handle variable requests and rich information extraction</li>
</ul>
</td>
</tr>
</tbody>
</table>
The plot below shows the impact of the different advancements in the word error rate with the bAbI task 1.
| ![diff_advancements](images/diff_advancements.png) |
|----|
Furthermore, it contains a set of rich analysis tools to get a deeper insight in the functionality of the ADNC. For example
that the advancements lead to a more meaningful gate usage of the memory cell.
|DNC|ADNC|
|---|---|
| ![process_dnc](images/function_DNC_2.png) | ![process_adnc](images/function_ADNC_2.png) |
## How to use:
### Setup ADNC
To install ADNC and setup an virtual environment:
```
git clone https://github.com/joergfranke/ADNC.git
cd ADNC/
python3 -m venv venv
source venv/bin/activate
pip install -e .
```
### Inference
For bAbI inference, choose pre-trained model (DNC, ADNC, BiADNC) in `scripts/inference_babi_task.py` and run:
`python scripts/inference_babi_task.py`
For CNN inference, choose pre-trained model (ADNC, BiADNC) in `scripts/inference_cnn_task.py` and run:
`python scripts/inference_babi_task.py`
### Training
t.b.a.
### Plots
t.b.a.
## Repository Structure
t.b.a.
## bAbI Results
| Task | DNC | EntNet | SDNC | ADNC | BiADNC | BiADNC<br>+aug16|
|----------------------------------|-----------------|------------------------|------------------------|------------------------|------------------------|--------------------------------------------------------|
| 1: 1 supporting fact | 9.0 ± 12.6 | 0.0 ± 0.1 | 0.0 ± 0.0 | 0.1 ± 0.0 | 0.1 ± 0.1 | 0.1 ± 0.0 |
| 2: 2 supporting facts | 39.2 ± 20.5 | 15.3 ± 15.7 | 7.1 ± 14.6 | 0.8 ± 0.5 | 0.8 ± 0.2 | 0.5 ± 0.2 |
| 3: 3 supporting facts | 39.6 ± 16.4 | 29.3 ± 26.3 | 9.4 ± 16.7 | 6.5 ± 4.6 | 2.4 ± 0.6 | 1.6 ± 0.8 |
| 4: 2 argument relations | 0.4 ± 0.7 | 0.1 ± 0.1 | 0.1 ± 0.1 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 |
| 5: 3 argument relations | 1.5 ± 1.0 | 0.4 ± 0.3 | 0.9 ± 0.3 | 1.0 ± 0.4 | 0.7 ± 0.1 | 0.8 ± 0.4 |
| 6: yes/no questions | 6.9 ± 7.5 | 0.6 ± 0.8 | 0.1 ± 0.2 | 0.0 ± 0.1 | 0.0 ± 0.0 | 0.0 ± 0.0 |
| 7: counting | 9.8 ± 7.0 | 1.8 ± 1.1 | 1.6 ± 0.9 | 1.0 ± 0.7 | 1.0 ± 0.5 | 1.0 ± 0.7 |
| 8: lists/sets | 5.5 ± 5.9 | 1.5 ± 1.2 | 0.5 ± 0.4 | 0.2 ± 0.2 | 0.5 ± 0.3 | 0.6 ± 0.3 |
| 9: simple negation | 7.7 ± 8.3 | 0.0 ± 0.1 | 0.0 ± 0.1 | 0.0 ± 0.0 | 0.1 ± 0.2 | 0.0 ± 0.0 |
| 10: indefinite knowledge | 9.6 ± 11.4 | 0.1 ± 0.2 | 0.3 ± 0.2 | 0.1 ± 0.2 | 0.0 ± 0.0 | 0.0 ± 0.1 |
| 11: basic coreference | 3.3 ± 5.7 | 0.2 ± 0.2 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 |
| 12: conjunction | 5 ± 6.3 | 0.0 ± 0.0 | 0.2 ± 0.3 | 0.0 ± 0.0 | 0.0 ± 0.1 | 0.0 ± 0.0 |
| 13: compound coreference | 3.1 ± 3.6 | 0.0 ± 0.1 | 0.1 ± 0.1 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.0 ± 0.0 |
| 14: time reasoning | 11 ± 7.5 | 7.3 ± 4.5 | 5.6 ± 2.9 | 0.2 ± 0.1 | 0.8 ± 0.7 | 0.3 ± 0.1 |
| 15: basic deduction | 27.2 ± 20.1 | 3.6 ± 8.1 | 3.6 ± 10.3 | 0.1 ± 0.1 | 0.1 ± 0.1 | 0.1 ± 0.1 |
| 16: basic induction | 53.6 ± 1.9 | 53.3 ± 1.2 | 53.0 ± 1.3 | 52.1 ± 0.9 | 52.6 ± 1.6 | 0.0 ± 0.0 |
| 17: positional reasoning | 32.4 ± 8 | 8.8 ± 3.8 | 12.4 ± 5.9 | 18.5 ± 8.8 | 4.8 ± 4.8 | 1.5 ± 1.8 |
| 18: size reasoning | 4.2 ± 1.8 | 1.3 ± 0.9 | 1.6 ± 1.1 | 1.1 ± 0.5 | 0.4 ± 0.4 | 0.9 ± 0.5 |
| 19: path finding | 64.6 ± 37.4 | 70.4 ± 6.1 | 30.8 ± 24.2 | 43.3 ± 36.7 | 0.0 ± 0.0 | 0.1 ± 0.1 |
| 20: agent’s motivation | 0.0 ± 0.1 | 0.0 ± 0.0 | 0.0 ± 0.0 | 0.1 ± 0.1 | 0.1 ± 0.1 | 0.1 ± 0.1 |
| __Mean WER:__ | 16.7 ± 7.6 | 9.7 ± 2.6 | 6.4 ± 2.5 | 6.3 ± 2.7 | 3.2 ± 0.5 | 0.4 ± 0.3 |
| __Failed Tasks (<5%):__ | 11.2 ± 5.4 | 5.0 ± 1.2 | 4.1 ± 1.6 | 3.2 ± 0.8 | 1.4 ± 0.5 | 0.0 ± 0.0 |