Arabic machine transliteration using an attention-based encoder-decoder model

Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been made towards improving this process for many languages such as English, French and Chinese, little research work has been accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area

Arabic machine transliteration using an attention-based encoder-decoder model

Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been made towards improving this process for many languages such as English, French and Chinese, little research work has been accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

Machine translation has two important parts, a learning process which followed by a translation process. Unfortunately, most of the translation process requires complex operations and in-depth knowledge of the languages in order to give a good quality translation. This study proposes a better approach, which does not require in-depth knowledge of the linguistic properties of the languages, but it produces a good quality translation. This study evaluated 28 different parameters in IRSTLM language modeling, which resulting 270 millions experiments, and proposes a sequence evaluation mechanism based on a maximum evaluation of each parameter in producing a good quality translation based on NIST and BLEU. The parallel corpus and statistical machine learning for English and Bahasa Indonesia were used in this study. The pruning process, user interface, and the personalization of translation have a very important role in implementing of this machine translation. The result is quite promising. It shows that pruning process increases of the translation process time. The particular sequence knowledge/value parameter in translation process has a better performance than the other method using in-depth linguistic knowledge approaches. All these processes, including the process of parsing from a stand-alone mode to an online mode, are also discussed in detail

The Development of Learning Mobile Application of Latin-to-Balinese Script Transliteration

Balinese script writing allegedly towards extinction. The general objective of this research is to preserve this Balinese culture aspect through a technology approach. The specific objective is to develop learning mobile application of Latin-To-Balinese script transliteration, as one of the innovative technology products of the Universitas Pendidikan Ganesha – Bali, and to analyze crowd feedback of that application to know the user acceptance and to obtain feedback for future improvement. This android-based application can be used as a learning media at mandatory local content subject Balinese Language in elementary and secondary schools in Province Bali since so far, it could handle the transliteration complex behaviors based on document "The Balinese Alphabet" with about 98% accuracy level (148 right of 151 test case). The development of this application was based on Model-View-Controller design pattern and use dictionary data structure to hold special words. Crowd feedback data was obtained through Google Play Console in five months since the application release. There are more than 32 thousand installations and 152 ratings (63 ratings with reviews). Average rating score of 4.2 (from the maximum best score of 5) and positive general comments (57% from total review) reflects relatively good of the user acceptance to the application. Further improvement priority of the application based on top three feedback category, i.e. addition of: 1) copy-paste-share feature (18% of reviews total); 2) compatibility to the old version of android (5% of reviews total); and 3) character “ě” insertion feature for generating sign pepet of Aksara Bali (3% of reviews total)