https://github.com/jimfleming/recurrent-entity-networks

TensorFlow implementation of "Tracking the World State with Recurrent Entity Networks".
https://github.com/jimfleming/recurrent-entity-networks

deep-learning machine-learning natural-language-processing recurrent-neural-networks tensorflow

Last synced: 3 months ago
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TensorFlow implementation of "Tracking the World State with Recurrent Entity Networks".

• Host: GitHub
• URL: https://github.com/jimfleming/recurrent-entity-networks
• Owner: jimfleming
• License: mit
• Created: 2016-11-20T00:34:37.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-11-02T20:31:27.000Z (about 7 years ago)
• Last Synced: 2024-05-03T06:21:03.010Z (6 months ago)
• Topics: deep-learning, machine-learning, natural-language-processing, recurrent-neural-networks, tensorflow
• Language: Python
• Homepage: https://arxiv.org/abs/1612.03969
• Size: 319 KB
• Stars: 274
• Watchers: 17
• Forks: 69
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Recurrent Entity Networks

This repository contains an independent TensorFlow implementation of recurrent entity networks from [Tracking the World State with

Recurrent Entity Networks](https://arxiv.org/abs/1612.03969). 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](https://github.com/facebook/MemNN/tree/master/EntNet-babi).



## 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 | ?

4: 2 argument relations | 0 | 0

5: 3 argument relations | 0.3 | ?

6: yes/no questions | 0.2 | 0

7: counting | 0 | 0

8: lists/sets | 0.5 | 0

9: simple negation | 0.1 | 0

10: indefinite knowledge | 0.6 | 0

11: basic coreference | 0.3 | 0

12: conjunction | 0 | 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

**Failed Tasks** | 0 | ?

**Mean Error** | 0.5 | ?

NOTE: Some of these tasks (16 and 19, in particular) required a change in learning rate schedule to reliably converge.

## Setup

1. Download the datasets by running [download_babi.sh](download_babi.sh) or from [The bAbI Project](https://research.facebook.com/research/babi/).

2. Run [prep_data.py](entity_networks/prep_data.py) which will convert the datasets into [TFRecords](https://www.tensorflow.org/programmers_guide/reading_data#standard_tensorflow_format).

3. Run `python -m entity_networks.main` to begin training on QA1.

## Major Dependencies

- TensorFlow v1.1.0

(For additional dependencies see [requirements.txt](requirements.txt))

## Thanks!

- Thanks to Mikael Henaff for providing details about their paper over Thanksgiving break. :)

- Thanks to Andy Zhang ([@zhangandyx](https://twitter.com/zhangandyx)) for helping me troubleshoot numerical instabilities.

- Thanks to Mike Young ([@mikalyoung](https://github.com/mikalyoung)) for providing results on some of the longer tasks.