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Neural Machine Translation for Turkish to English Using Deep Learning

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Digital Interaction and Machine Intelligence (MIDI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1376))

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Abstract

Machine translation is one of the challenging Artificial Intelligence areas due to the complexity of the languages. Models that use Recurrent Neural Networks (RNN) such as Sequence-to-Sequence model have become popular on many Natural Language Processing (NLP) problems including machine translation. However, these models underperform on long sentences due to fixed length vector design. In this paper, we deployed a RNN based Sequence-to-Sequence model and Long Short-Term Memory (LSTM) with attention trained for Machine Translation (MT). We evaluated our model on a Turkish to English dataset. In addition, we have created a dataset with words from Ottoman literature which we plan to use in our future studies. We believe that this dataset will be a very valuable data source as not many NLP studies have been done with Ottoman literature. The effect of hyperparameters is also examined.

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Correspondence to Fatih Balki .

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Balki, F., Demirhan, H., Sarp, S. (2021). Neural Machine Translation for Turkish to English Using Deep Learning. In: Biele, C., Kacprzyk, J., Owsiński, J.W., Romanowski, A., Sikorski, M. (eds) Digital Interaction and Machine Intelligence. MIDI 2020. Advances in Intelligent Systems and Computing, vol 1376. Springer, Cham. https://doi.org/10.1007/978-3-030-74728-2_1

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