This is Implementation For the Course Project for NLA CS573 Spring 2020. English to Simple English - Machine translation

Team: Karthikeyan and Suman Mentor : Prashant Kodali and Manish Shrivastava

Data : https://cs.pomona.edu/~dkauchak/simplification/

Papers :

1. Simple English Wikipedia: A New Text Simplification Task – William and Kauchak https://cs.pomona.edu/~dkauchak/papers/kauchak11simple.short.pdf
2. Improving Text Simplification Language Modeling Using Unsimplified Text Data – David Kauchak - https://www.aclweb.org/anthology/P13-1151.pdf
3. Simple and Effective Text Simplification Using Semantic and Neural Methods – Elior et al – https://wwww.aclweb.org/anthology/P18-1016.pdf
4. Exploring Neural Text Simplification Models – Sergiu et al – https://www.aclweb.org/anthology/P17-2014.pdf

Appraoch :

Baseline SMT :

1. Create a word based Translation Probablity model
2. Create a Language Model
3. Combine Transaltion Probablity and Language model with H.M.M to Create a Functional SMT Model
4. This model has Bleu Score of 0.1499

NMT Approach :

1. Seq 2 Seq - Model Building Task Failed because of computational requirements. Unable to make the model converge to get a non giberrish results
2. Transformers Approach : Able to Build a Transformer model with ~12500 Sentence Pair sub data. We created two different runs of our transformer model with different number of epochs 2.1) for First run with 20 Epochs - Model has Bleu Score of 0.2148 2.2) for Second Run with 28 Epochs - Model has bleu Score of 0.2303 ....