Recurrent Entity Networks

This repository contains an independent TensorFlow implementation of recurrent entity networks from Tracking the World State with Recurrent Entity Networks. This paper introduces the first method to solve all of the bAbI tasks using 10k training examples. The author's original Torch implementation is available here.

Results

Percent error for each task, comparing those in the paper to the implementation contained in this repository.

Task	EntNet (paper)	EntNet (repo)
1: 1 supporting fact	0	0
2: 2 supporting facts	0.1	3.0
3: 3 supporting facts	4.1	55
4: 2 argument relations	0	0
5: 3 argument relations	0.3	0.5
6: yes/no questions	0.2	0.1
7: counting	0	1.6
8: lists/sets	0.5	0.5
9: simple negation	0.1	0.7
10: indefinite knowledge	0.6	0.1
11: basic coreference	0.3	0
12: conjunction	0	0
13: compound coreference	1.3	0
14: time reasoning	0	4.5
15: basic deduction	0	0
16: basic induction	0.2	0
17: positional reasoning	0.5	1.7
18: size reasoning	0.3	1.5
19: path finding	2.3	41.9
20: agents motivation	0	0.2
Failed Tasks	0	?
Mean Error	0.5	?

Setup

1. Download the datasets by running download_datasets.sh or from The bAbI Project.
2. Run prep_datasets.py which will convert the datasets into TFRecords.
3. Run python -m entity_networks.main to begin training on QA1.
4. Run ./run_all.sh to train on all tasks.

Dependencies

• TensorFlow v1.1