A Reverse Positional Encoding Multi-Head Attention-Based Neural Machine Translation Model for Arabic Dialects

Laith H. Baniata (), Sangwoo Kang () and Isaac. K. E. Ampomah
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Laith H. Baniata: School of Computing, Gachon University, Seongnam 13120, Korea
Sangwoo Kang: School of Computing, Gachon University, Seongnam 13120, Korea
Isaac. K. E. Ampomah: Shell Center, 2 York Road, London SE1 7LZ, UK

Mathematics, 2022, vol. 10, issue 19, 1-25

Abstract: Languages with a grammatical structure that have a free order for words, such as Arabic dialects, are considered a challenge for neural machine translation (NMT) models because of the attached suffixes, affixes, and out-of-vocabulary words. This paper presents a new reverse positional encoding mechanism for a multi-head attention (MHA) neural machine translation (MT) model to translate from right-to-left texts such as Arabic dialects (ADs) to modern standard Arabic (MSA). The proposed model depends on an MHA mechanism that has been suggested recently. The utilization of the new reverse positional encoding (RPE) mechanism and the use of sub-word units as an input to the self-attention layer improve this sublayer for the proposed model’s encoder by capturing all dependencies between the words in right-to-left texts, such as AD input sentences. Experiments were conducted on Maghrebi Arabic to MSA, Levantine Arabic to MSA, Nile Basin Arabic to MSA, Gulf Arabic to MSA, and Iraqi Arabic to MSA. Experimental analysis proved that the proposed reverse positional encoding MHA NMT model was efficiently able to handle the open grammatical structure issue of Arabic dialect sentences, and the proposed RPE MHA NMT model enhanced the translation quality for right-to-left texts such as Arabic dialects.

Keywords: reverse positional encoding (RPE); multi-head attention; neural machine translation (NMT); Arabic dialects; MSA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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