This document presents a language translation system using machine learning. It discusses using recurrent neural networks (RNNs) with an encoder-decoder model to translate text from a source language to a target language. The system first pre-processes and cleans the data before building and training the RNN model. It then generates translations and iterates on the model to improve accuracy. Specifically, one RNN layer acts as an encoder to process the source language input, while another acts as a decoder to output the translated text in the target language. The goal of this project is to create an automated machine translation system using deep learning techniques.

1. Language Translator
Presented by:
M. Chaitanya Kumar
M. GangaVasudha
Using machine learning

2. Abstract
LanguageTranslator with machine means automatic
translation, It the field of Artificial Intelligence. It is
computer program which is design to translate text from
one language (source language) to another language
(target language) without the help of human.The aim of
LanguageTranslation is to provide a system that
translate text of source language into target language
and translation express the same meaning as it in source
language

3. Why Machine Translation
• business: international trade,
investment, contracts, finance
• commerce: travel, purchase of
foreign goods and services,
customer support
• media: accessing information via
search, sharing information via
social networks, localization of
content and advertising
• education: sharing of ideas,
collaboration, translation of
research papers
• government: foreign relations,
negotiation

4. Building the pipeline
1.Pre-processing: load and examine data, cleaning,
tokenization, padding
2.Modelling: build, train, and test the model
3.Prediction: generate specific translations of source
to target language, and compare the output
translations to the ground truth translations
4.Iteration: iterate on the model, experimenting with
different architectures

5. For implementing, we will create two RNN layer :
One RNN layer will act as ‘encoder’: In this we give our source
language sentence as an input.
And other RNN layer will act as ‘decoder’: which will give us the
output (translated sentence in target language)

6. summary
▪ This is very interesting as well as a complex Machine Learning
project. In this we have learned many new concepts and also
about Recurrent neural networks (RNNs), LSTM layers, how to
implement LSTM layers. We’ve also understood the concept
behind encoder and decoder and creating RNN models .
And after learning all this we have finally created a Language
Translator which translates source language text to target
language.

7. References
▪ [1] M. Anand Kumar, B. Premjith, S. Shivkaran, B. Kavirajan, S. Rajendran and K. P.
Soman, Overview of the shared task on
▪ machine translation in Indian languages (MTIL-2017), J. Intell. Syst. (2017).
▪ [2] M. Auli, M. Galley, C. Quirk and G. Zweig, Joint language and translation modeling
with recurrent neural networks, in:
▪ EMNLP, vol. 3, Association of Computational Linguistics, Seattle, WA, USA, 2013.
▪ [3] S. Chandar AP, S. Lauly, H. Larochelle, M. Khapra, B. Ravindran, V. C. Raykar and
A. Saha, An autoencoder approach to
▪ learning bilingual word representations, in: Advances in Neural Information Processing
Systems, pp. 1853–1861, MIT Press,
▪ Cambridge, MA, USA, 2014.
▪ [4] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk
and Y. Bengio, Learning phrase representa-

8. Thank you