Text Normalization using Memory Augmented Neural Networks
The Text Normalization Demo notebook and the accompanying paper "Text Normalization using Memory Augmented Neural Networks" demonstrates an accuracy of 99.5% on the Text Normalization Challenge by Richard Sproat and Navdeep Jaitly. An earlier version of the approach used here has secured the 6th position in the Kaggle Russian Text Normalization Challenge by Google's Text Normalization Research Group.
Go straight to the Text Normalization Demo Notebook
Architecture
Two models are used for the purpose of text normalization. A XGBoost boost model first classifies a token as to-be-normalized or remain-self. The to-be-normalized tokens are then fed character-by-character to our proposed Sequence to Sequence DNC model.
More details about the architecture and implementation can be found in the original paper.
**Sequence to sequence DNC**
Results :
1. Normalizing English Data
**Semiotic Classwise Accuracy**
| semiotic-class | accuracy | count | correct |
|----------------|--------------------|-------|---------|
| ALL | 0.994267233453397 | 92451 | 91921 |
| ADDRESS | 1.0 | 4 | 4 |
| CARDINAL | 0.9942140790742526 | 1037 | 1031 |
| DATE | 0.9971751412429378 | 2832 | 2824 |
| DECIMAL | 0.9891304347826086 | 92 | 91 |
| DIGIT | 0.7954545454545454 | 44 | 35 |
| ELECTRONIC | 0.7346938775510204 | 49 | 36 |
| FRACTION | 0.6875 | 16 | 11 |
| LETTERS | 0.971611071682044 | 1409 | 1369 |
| MEASURE | 0.971830985915493 | 142 | 138 |
| MONEY | 0.972972972972973 | 37 | 36 |
| ORDINAL | 0.9805825242718447 | 103 | 101 |
| PLAIN | 0.9939611747724394 | 67894 | 67484 |
| PUNCT | 0.9988729854615125 | 17746 | 17726 |
| TELEPHONE | 0.918918918918919 | 37 | 34 |
| TIME | 0.75 | 8 | 6 |
| VERBATIM | 0.994005994005994 | 1001 | 995 |
2. Normalizing Russian Data
Semiotic Classwise Accuracy
| semiotic-class | accuracy | count | correct |
|----------------|--------------------|-------|---------|
| ALL | 0.9928752306965964 | 93196 | 92532 |
| CARDINAL | 0.9417922948073701 | 2388 | 2249 |
| DATE | 0.9732441471571907 | 1495 | 1455 |
| DECIMAL | 0.9 | 60 | 54 |
| DIGIT | 1.0 | 16 | 16 |
| ELECTRONIC | 0.6041666666666666 | 48 | 29 |
| FRACTION | 0.6086956521739131 | 23 | 14 |
| LETTERS | 0.9907608695652174 | 1840 | 1823 |
| MEASURE | 0.8978102189781022 | 411 | 369 |
| MONEY | 0.8947368421052632 | 19 | 17 |
| ORDINAL | 0.9461358313817331 | 427 | 404 |
| PLAIN | 0.994688407139769 | 64764 | 64420 |
| PUNCT | 0.9998519542045006 | 20264 | 20261 |
| TELEPHONE | 0.8202247191011236 | 89 | 73 |
| TIME | 0.75 | 8 | 6 |
| VERBATIM | 0.9985119047619048 | 1344 | 1342 |
How to run?
Requirements:
Follow these steps for a demonstration:
- Clone the repo
- Download and extract the required data.
$ sh setup.sh
- Create & activate an environment using the provided file
$ conda env create -f environment.yml
$ source activate deep-tf
- Start a Jupyter Notebook server
- Open 'notebooks/Text Normalization Demo.ipynb'
- Set the language to English or Russian below the 'Global Config' cell
lang = 'english'
# lang = 'russian'
- Run the notebook
Full Requirements:
- numpy 1.13.3
- pandas 0.21.0
- matplotlib 2.1.0
- watermark 1.5.0
- seaborn 0.8.1
- sklearn 0.19.1
- xgboost 0.6
- tensorflow 1.3.0
Authors
- Subhojeet Pramanik (http://github.com/subho406)
- Aman Hussain (https://github.com/AmanDaVinci)
Acknowledgements
Differentiable Neural Computer, Tensorflow Implementation: https://github.com/deepmind/dnc