Abstract:
The demand of language translation is growing rapidly. Modern technology is offering the
translation service for several languages. But the translation of different Bengali dialects is
not very well explored filled in NLP. The goal of our work is to develop a proper
conversion system which will convert the local form of Bangla spoken by the people of
Chittagong to the standard Bangla language. The dialect of Chittagong district is one of
the most different dialect in Bangladesh. We will use Natural Language Processing and
Machine Learning to build an efficient system to convert the dialect to the standard form
of Bangla language. So that a person who is not from Chittagong can easily communicate
with the locals of Chittagong. To build this system we have used an LSTM based encoder
decoder model. This model consists of LSTM, Embedding, Repeat Vector, Time
Distributed dense layers. We have achieved 99.3% model accuracy for training and 72.5%
model accuracy for testing sets. The accuracy we have achieved from our system is quite
good. But the accuracy can rise if we can reduce our data loss more. As the data loss and
the accuracy of our work is totally depending on the volume of the dataset. With time we
will increase the volume of our existing dataset.
