Improving English to Persian Machine Translation with GPT Language Model and Autoencoders

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Dorosti Maryam; Pedram Mir Mohsen

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Improving English to Persian Machine Translation with GPT Language Model and Autoencoders

Author(s)

Dorosti Maryam | Pedram Mir Mohsen

Keywords

Machine Translation

Deep Learning

Language Model

Autoencoder

Transformer

Abstract

Machine Translation is a technology that reduces costs and speeds up the translation for users by mechanizing translation from one language to another. Machine Translation is essentially a step towards globalization for every culture, science, industry, and system. This technology has made great strides in cognitive understanding of natural language since 2013 with the advent of Deep Learning models. But deep models also always need a lot of data for training. Therefore, the lack of a lot of data and parallel corpus in Machine Translation is one of the most important problems in this area. Machine Translation from English to Persian always suffers from the problem of a lack of resources and data. This article tries to study the Deep Learning models in Machine Translation from English to Persian and their strengths and weaknesses, In this article, to solve the problem of lack of English-to-Persian data, the Transformer-based model has been integrated and improved with the Persian Language Model that has GPT architecture, in addition, the CNN model has been integrated and improved with Autoencoder to improve feature selection and reduce dimensions.

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APA: Copy

Dorosti, Maryam, & Pedram, Mir Mohsen. (). . . SID. https://sid.ir/paper/1047275/en

Vancouver: Copy

Dorosti Maryam, Pedram Mir Mohsen. . . Available from: https://sid.ir/paper/1047275/en

IEEE: Copy

Maryam Dorosti, and Mir Mohsen Pedram, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1047275/en

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