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:

• Jupyter Notebook
• Anaconda Package Manager
• rest will be installed by anaconda (see below)

Follow these steps for a demonstration:

1. Clone the repo
2. Download and extract the required data.

$ sh setup.sh

2. Create & activate an environment using the provided file

$ conda env create -f environment.yml
$ source activate deep-tf

3. Start a Jupyter Notebook server
4. Open 'notebooks/Text Normalization Demo.ipynb'
5. Set the language to English or Russian below the 'Global Config' cell

lang = 'english'
# lang = 'russian'

6. 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

1. Subhojeet Pramanik (https://github.com/subho406)
2. Aman Hussain (https://github.com/AmanDaVinci)

Acknowledgements

Differentiable Neural Computer, Tensorflow Implementation: https://github.com/deepmind/dnc