transfer-based approach

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Shaalan, K., and A. H. Hossny, "Automatic rule induction in Arabic to English machine translation framework", Challenges for Arabic Machine Translation, Amsterdam, The Netherlands, John Benjamins Publishing Company, 2012. Abstractkhaled_shaalan_ch10.pdf

This paper addresses exploiting a supervised machine learning technique to automatically induce Arabic-to-English transfer rules from chunks of parallel aligned linguistic resources. The induced structural transfer rules encode the linguistic translation knowledge for converting an Arabic syntactic structure into a target English syntactic structure. These rules are going to be an integral part of an Arabic-English transfer-based machine translation. Nevertheless, a novel morphological rule induction method is employed for learning Arabic morphological rules that are applied in our Arabic morphological analyzer. To demonstrate the capability of the automated rule induction technique we conducted rule-based translation experiments that use induced rules from a relatively small data set. The translation quality of the hybrid translation experiments achieved good results in terms of WER.

Shaalan, K., A. Hendam, and A. Rafea, "Rapid development and deployment of bi-directional expert systems using machine translation technology", Expert Systems with Applications, vol. 39, issue 1, no. 1, pp. 1375 - 1380, 2012. Abstractks_mt_eswa_2012.pdfWebsite

The present work reports our attempt in developing an English–Arabic bi-directional machine translation tool in the agriculture domain. It aims to achieve automated translation of agricultural expert systems. In particular, we describe the translation of domain knowledge base, including, prompts, responses, explanation text, and advices. In the Central Laboratory for Agricultural Expert Systems (CLAES) where many successful agricultural expert systems have been developed, this tool is found to be essential in developing bi-directional (English–Arabic) expert systems because both English and Arabic versions are needed for development, deployment, and usage purpose. The tool also helps knowledge engineers in overcoming the language barrier by acquiring knowledge from either English or Arabic speaking domain experts. This paper discusses our experience with the developed machine translation tool and reports on results of its application on real agricultural expert systems.

Shaalan, K., H. Bakr, and I. Ziedan, "Transferring Egyptian Colloquial Dialect into Modern Standard Arabic", International Conference on Recent Advances in Natural Language Processing (RANLP 2007), Borovets, Bulgaria, John Benjamins, pp. 525–529, sep, 2007. Abstracttransferring_egyptian_colloquial2arabic_.pdf

Arabic is rooted in the Classical or Qur'anical Arabic, but over the centuries, the language has developed to what is now accepted as Modern Standard Arabic (MSA). Arab colloquial dialects are generally only spoken languages, but recently the rate of colloquial written text increases dramatically as a medium of expressing ideas especially across the WWW, usually in the form of blogs and partially colloquial articles. Most of these written colloquial has been in the Egyptian colloquial dialect, which is considered the most widely dialect understood and used throughout the Arab world. We are able to reuse MSA processing tools with colloquial Arabic by transferring colloquial Arabic words into their corresponding MSA words. The advantages of this lexical transfer are to facilitate the communication with colloquial Arabic speakers and restoring it to the standard language in use nowadays. This paper addresses the transfer techniques between colloquial Arabic and MSA, which have not yet been closely studied before. In particular, we present a rule-based lexical transfer approach for converting Egyptian colloquial words into their corresponding MSA words. This process involves morphological analysis and lexical acquisition of colloquial words.

Shaalan, K., A. Rafea, A. Abdel-Moneim, and H. Baraka, "Machine Translation of English Noun Phrases into Arabic", The International Journal of Computer Processing of Oriental Languages, vol. 17, no. 2, pp. 121–134, 2004. Abstractmt_nlp.pdfWebsite

The present work reports our attempt in automating the translation of English noun phrase (NP) into Arabic. Translating NP is a very important task toward sentence translation since NPs form the majority of textual content of the scientific and technical documents. The system is implemented in Prolog and the parser is written in DCG formalism. The paper also describes our experience with the developed MT system and reports results of its application on real titles of theses from the computer science domain.