Abstract
Automatic Machine Translation is a computer application that automatically translates one source-language sentence into the corresponding target-language sentence. With the increased volume of user-generated content on the web, textual information becomes freely available and with a gigantic quantity. Hence, it is becoming increasingly common to adopt automated analysis tools from Machine Learning (ML) to represent such kind of information. In this paper, we propose a new method called Enhanced Word Vectors (EWVs) generated using Word2vec and FastText models. These EWVs are then used for training and testing a new Deep Learning (DL) architecture based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Moreover, special preprocessing of the Arabic sentences is carried out. The performance of the proposed scheme is validated and compared with Word2vec and FastText using UN dataset. From the experimental results, we find that in most of the cases, our proposed approach achieves the best results, compared to Word2vec and FastText models alone.
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Bensalah, N., Ayad, H., Adib, A., El Farouk, A.I. (2021). Arabic Machine Translation Based on the Combination of Word Embedding Techniques. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_17
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