Developing English to Dawurootsuwa Machine Translation Model Using RNN
Domain:
natural language processingRecord type:
modelpaper
Creator:
Ase
Editor:
Hus
Publisher:
Nat
Host:
The idea of language translation is developing recently to solve the issues of linguistic diversity.
The translation of English texts into Amharic, Afaan-Oromoo, Tigrigna, China, French, and
Somalia are developed. However, as the knowledge of the researchers is concerned, English to
Dawurootsuwa machine translation is not developed. This thesis aims to develop unidirectional
English to the Dawurootsuwa machine translation model by using Neural Network (NN)
approaches. In NN approach Recurrent Neural network is a model that designed to recognize and
analyze patterns in sequences of data. It predicted the output text based on the current input text
and previous output text. Under in RNN LSTM and GRU contain a neuron, each neuron are
replaced with cells having control gates. That used for a memory cell that maintains gates to
manage the flow of sentences inaccurate order and fully connected to the model. A parallel corpus,
which consists of 20,345 pairs of sentences is prepared from different sources and classified as a
90% training set and a 10% test set. A recurrent Neural Network model with 22 input nodes and
27 output nodes is developed and implemented using Keras toolkit of Python programing language
and Adam algorithm. Totally they are contain four results based automatic (BLEU) score and
manual evaluation (Arithmetic Mean Value) techniques with hidden layer size of 2. In simple RNN
model the BLEU score is 0.5187 with the learning rate of 0.002 and AMV result is 0.60914. In
embedding RNN model the BLEU score is 0.5245 with the learning rate of 0.003 and AMV result
is 0.60914. In bidirectional RNN model the BLEU score is 0.5452 with the learning rate of 0.004
and AMV result is 0. 0.60914. Finally, in encoder-decoder model the BLEU score is 0.555 with
the learning rate of 0.005 and AMV result is 0.60914. And after 0.005 learning rate there is similar
score were recorded with the maximum threshold epochs of 100. From the result, concluded that,
encoder decoder model of BLEU score 0.555 is fairly good accuracy achieved compare from the
rest model and less achieved comparatively from AMV result. In further work to encourage
English-Dawurootsuwa parallel corpus improve the accuracy more and minimize the loss and.
develop the model from unidirectional to the multidirectional language model.
The translation of English texts into Amharic, Afaan-Oromoo, Tigrigna, China, French, and
Somalia are developed. However, as the knowledge of the researchers is concerned, English to
Dawurootsuwa machine translation is not developed. This thesis aims to develop unidirectional
English to the Dawurootsuwa machine translation model by using Neural Network (NN)
approaches. In NN approach Recurrent Neural network is a model that designed to recognize and
analyze patterns in sequences of data. It predicted the output text based on the current input text
and previous output text. Under in RNN LSTM and GRU contain a neuron, each neuron are
replaced with cells having control gates. That used for a memory cell that maintains gates to
manage the flow of sentences inaccurate order and fully connected to the model. A parallel corpus,
which consists of 20,345 pairs of sentences is prepared from different sources and classified as a
90% training set and a 10% test set. A recurrent Neural Network model with 22 input nodes and
27 output nodes is developed and implemented using Keras toolkit of Python programing language
and Adam algorithm. Totally they are contain four results based automatic (BLEU) score and
manual evaluation (Arithmetic Mean Value) techniques with hidden layer size of 2. In simple RNN
model the BLEU score is 0.5187 with the learning rate of 0.002 and AMV result is 0.60914. In
embedding RNN model the BLEU score is 0.5245 with the learning rate of 0.003 and AMV result
is 0.60914. In bidirectional RNN model the BLEU score is 0.5452 with the learning rate of 0.004
and AMV result is 0. 0.60914. Finally, in encoder-decoder model the BLEU score is 0.555 with
the learning rate of 0.005 and AMV result is 0.60914. And after 0.005 learning rate there is similar
score were recorded with the maximum threshold epochs of 100. From the result, concluded that,
encoder decoder model of BLEU score 0.555 is fairly good accuracy achieved compare from the
rest model and less achieved comparatively from AMV result. In further work to encourage
English-Dawurootsuwa parallel corpus improve the accuracy more and minimize the loss and.
develop the model from unidirectional to the multidirectional language model.