Machine Translation

Abdel-Monem, A., K. Shaalan, A. Rafea, and H. Baraka, "Generating Arabic text in multilingual speech-to-speech machine translation framework", Machine Translation, vol. 22, no. 4, Hingham, MA, USA, Kluwer Academic Publishers, pp. 205–258, 2008. Abstractgenerating_arabic_mt_journal.pdfWebsite

The interlingual approach to machine translation (MT) is used successfully in multilingual translation. It aims to achieve the translation task in two independent steps. First, meanings of the source-language sentences are represented in an intermediate language-independent (Interlingua) representation. Then, sentences of the target language are generated from those meaning representations. Arabic natural language processing in general is still underdeveloped and Arabic natural language generation (NLG) is even less developed. In particular, Arabic NLG from Interlinguas was only investigated using template-based approaches. Moreover, tools used for other languages are not easily adaptable to Arabic due to the language complexity at both the morphological and syntactic levels. In this paper, we describe a rule-based generation approach for task-oriented Interlingua-based spoken dialogue that transforms a relatively shallow semantic interlingual representation, called interchange format (IF), into Arabic text that corresponds to the intentions underlying the speaker's utterances. This approach addresses the handling of the problems of Arabic syntactic structure determination, and Arabic morphological and syntactic generation within the Interlingual MT approach. The generation approach is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic generator is implemented in SICStus Prolog. We conducted evaluation experiments using the input and output from the English analyzer that was developed by the NESPOLE! team at Carnegie Mellon University. The results of these experiments were promising and confirmed the ability of the rule-based approach in generating Arabic translation from the Interlingua taken from the travel and tourism domain.

Hossny, A., K. Shaalan, and A. Fahmy, "Machine translation model using inductive logic programming", the 2009 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE’09), Dalian, China, pp. 1–8, sep, 2009. Abstract101.pdf

Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs Inductive Logic Programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%.

Shaalan, K., A. Abdel-Monem, and A. Rafea, "Syntactic Generation of Arabic in Interlingua-based Machine Translation Framework", Third workshop on Computational Approaches to Arabic Script-based Languages (CAASL3), Machine Translation Summit XII: ACL, 2009. Abstractsyntactic_gen_arabic_caasl3.pdf

Arabic is a highly inflectional language, with a rich morphology, relatively free word order, and two types of sentences: nominal and verbal. Arabic natural language processing in general is still underdeveloped and Arabic natural language generation (NLG) is even less developed. In particular, Arabic natural language generation from Interlingua was only investigated using template-based approaches. Moreover, tools used for other languages are not easily adaptable to Arabic due to the Arabic language complexity at both the morphological and syntactic levels. In this paper, we report our attempt at developing a rule-based Arabic generator for task-oriented interlingua-based spoken dialogues. Examples of syntactic generation results from the Arabic generator will be given and will illustrate how the system works. Our proposed syntactic generator has been effectively evaluated using real test data and achieved satisfactory results.

Shaalan, K., A. Hendam, and A. Rafea, "An English-Arabic Bi-directional Machine Translation Tool in the Agriculture Domain", Intelligent Information Processing V, vol. 340, Berlin, Heidelberg, Springer Boston, pp. 281–290, 2010. Abstractbi_direct_a_e_mt.pdf

The present work reports our attempt in developing an English-Arabic bi-directional Machine Translation (MT) tool in the agriculture domain. It aims to achieve automated translation of expert systems. In particular, we describe the translation of knowledge base, including, prompts, responses, explanation text, and advices. In the central laboratory for agricultural expert systems, 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 follows the rule-based transfer MT approach. A major design goal of this tool is that it can be used as a stand-alone tool and can be very well integrated with a general (English-Arabic) MT system for Arabic scientific text. The paper also discusses our experience with the developed MT system and reports on results of its application on real agricultural expert systems.

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