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 now available here.
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||?|
|4: 2 argument relations||0||0|
|5: 3 argument relations||0.3||?|
|6: yes/no questions||0.2||0|
|9: simple negation||0.1||0|
|10: indefinite knowledge||0.6||0|
|11: basic coreference||0.3||0|
|13: compound coreference||1.3||0|
|14: time reasoning||0||0|
|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||0|
|20: agents motivation||0||0|
NOTE: Some of these tasks (16 and 19, in particular) required a change in learning rate schedule to reliably converge.
- Download the datasets by running download_babi.sh or from The bAbI Project.
- Run prep_data.py which will convert the datasets into TFRecords.
python -m entity_networks.mainto begin training on QA1.
- TensorFlow v1.1.0
(For additional dependencies see requirements.txt)