Machine Translation
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.
Abstract 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.
AbstractThe 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., 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.
AbstractThe 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.
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.
AbstractArabic 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.
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.
AbstractRule 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%.
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.
AbstractThe 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.
Shaalan, K., A. Abdel-Monem, and A. Rafea,
"Arabic Morphological Generation from Interlingua: A Rule-based Approach",
Intelligent Information Processing III, vol. 228: Springer US, pp. 441-451, 2007.
AbstractArabic is a Semitic language that is rich in its morphology. Arabic has very numerous and complex morphological rules. Arabic morphological analysis has gained the focus of Arabic natural language processing research for a long time in order to achieve the automated understanding of Arabic. With the recent technological advances, Arabic natural language generation has received attentions in order to allow for a room for wider applications such as machine translation. For machine translation systems that support a large number of languages, interlingua-based machine translation approaches are particularly attractive. In this paper, we report our attempt at developing a rule-based Arabic morphological generator for task-oriented interlingua-based spoken dialogues. Examples of morphological generation results from the Arabic morphological generator will be given and will illustrate how the system works. Nevertheless, we will discuss the issues related to the morphological generation of Arabic words from an interlingua representation, and present how we have handled them.
Farouk, A., A. Rafea, and K. Shaalan,
"Recognizing Semantic Concepts of Spoken Arabic Utterances using Genetic Technology",
The Seventh Conference on Language Engineering, Egyptian Society of Language Engineering (ELSE), Cairo, Egypt, Ain Shams University, dec, 2007.
AbstractGenetic algorithms (GA) are a family of computational models inspired by evolution. GA mainly designed to solve optimization problems which can be thought of as searching through a large number of candidates for the best one that can be found. In this paper we present a genetic model to solve the problem of recognizing deep semantic concepts from spoken Arabic utterances. The aim of this algorithm is to automatically generate the grammar that recognizes each concept in the domain of discourse. This grammar is used to extract the observed concepts from the utterance. An experiment has been conducted to measure the performance of our approach. The results were promising and assured the ability of this approach in identifying the concepts of Arabic utterances taken from the travel and tourism domain.
Farouk, A., A. Rafea, and K. Shaalan,
"Analysis of Spoken Arabic into Interlingua Representation using Automatic Classification Approach",
3rd International Computer Engineering Conference: Smart Applications for the Information Society, Cairo, Egypt, Cairo University, dec, 2007.
AbstractSemantic analysis is the system that takes as input a sentence and outputs a list of prominent concepts that characterize the contents of the input sentence, and for each concept, gives the set of attributes that discuss the concept along with their relevancies. This paper presents a system that employs a machine learning approach that automates the semantic analysis process of spoken Arabic into interlingua representation. An experiment has been conducted to measure the performance of our approach. The results were promising and assured the ability of this approach in capturing the semantics of Arabic utterances taken from the travel and tourism domain.
Shaalan, K., A. A. Monem, A. Rafea, and H. Baraka,
"Generating Arabic text from Interlingua",
the 2nd Workshop on Computational Approaches to Arabic Script-based Languages (CAASL), Stanford, California, USA, Linguistic Society of America Summer Institute, Stanford University, pp. 137–144, jul, 2007.
AbstractIn this paper, we describe a grammar-based generation approach for task-oriented interlingua-based spoken dialogue that transforms a shallow semantic interlingua representation called Interchange Format (IF) into Arabic Text that corresponds to the intentions underlying the speakers' utterances. 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 an evaluation experiment using the output from the English analyzer provided by Carnegie Mellon University (CMU). The results of this experiment were promising and assured the ability of the generation approach in generating Arabic text form the interlingua taken from the travel and tourism domain.
