Syntactic Reordering of the source language to better match the phrase structure of the target language has been shown to improve the performance of phrase-based Statistical Machine Translation. This paper applies syntactic reordering to English-to-Arabic translation. It introduces reordering rules, and motivates them linguistically. It also studies the effect of combining reordering with Arabi...

BACKGROUND Functional Assessment of Cancer Therapy-Bone Marrow Transplant (FACT-BMT) has been translated from English into several languages. Currently, there is no validated translation of FACT-BMT in Arabic. Here, we are reporting the first Arabic translation and validation of the FACT-BMT. METHODS The study was approved by the Institutional Research Advisory Council. The Arabic translation...

We have developed an experimental Arabic-to-English example-based machine translation (EBMT) system, which exploits a bilingual corpus to find examples that match fragments of the input source-language text--Modern Standard Arabic (MSA), in our case--and imitates its translations. Translation examples were extracted from a collection of parallel, sentencealigned, unvocalized Arabic-English docu...

Modern Standard Arabic (MSA) is the formal language in most Arabic countries. Arabic Dialects (AD) or daily language differs from MSA especially in social media communication. However, most Arabic social media texts have mixed forms and many variations especially between MSA and AD. This paper aims to bridge the gap between MSA and AD by providing a framework for the translation of texts of soc...

Discourse connectives can often signal multiple discourse relations, depending on their context. The automatic identification of the Arabic translations of seven English discourse connectives shows how these connectives are differently translated depending on their actual senses. Automatic labelling of English source connectives can help a machine translation system to translate them more corre...

In this paper, we focus on a sub-problem of Arabic text error correction, namely Arabic Text Denormalization. Text Denormalization is considered an important post-processing step when performing machine translation into Arabic. We examine different approaches for denormalization via the use of language modeling, stemming, and sequence labeling. We show the effectiveness of different approaches ...

Discourse connectives can often signal multiple discourse relations, depending on their context. The automatic identification of the Arabic translations of seven English discourse connectives shows how these connectives are differently translated depending on their actual senses. Automatic labelling of English source connectives can help a machine translation system to translate them more corre...

In TREC 2002 the Berkeley group participated only in the English-Arabic cross-language retrieval (CLIR) track. One Arabic monolingual run and three English-Arabic cross-language runs were submitted. Our approach to the crosslanguage retrieval was to translate the English topics into Arabic using online English-Arabic machine translation systems. The four official runs are named as BKYMON, BKYCL...

This study aims to compare the effectiveness of two popular machine translation systems (Google Translate and Babylon machine translation system) used to translate English sentences into Arabic relative to the effectiveness of English to Arabic human translation. There are many automatic methods used to evaluate different machine translators, one of these methods; Bilingual Evaluation Understud...

The present study aims to provide a contrastive analysis of Arabic-English translation ten legal texts with an eye evaluating the accuracy translation. researcher collected data from El-Farahaty’s (2015) Arabic–English–Arabic Legal Translation. A was developed assess selected. examination source and translations provided either by authoress book herself or sources which she them revealed seriou...