This paper passes under review the history of WordNets and introduces some of the most prominent projects of Word Net construction in order to illustrate how these tools can be employed as lexicon in finding equivalents in machine translation projects. Then the process of designing and developing the Persian noun WordNet-as a part of the Persian WordNet which is an important tool for processing Persian language-is discussed.At the end, the applications of Word Nets in general and more specifically their application in machine translation are put forward.

This paper tries to introduce the notion of 'abstract representation', one of the key notions in machine translation (MT).This aim is going to be gained through giving some examples of abstract representation which have been found in different references. In the first systems of MT, experts in computer sciences and linguists tried to use 'abstract representation' to give a proper translation, but because of the complexity of this notion and the problems which early computers faced, some of them decided to forsake 'abstract representation' totally. They started using methods without abstract representation such as example-based and statistical approaches, but after a while, these methods, too, failed to solve the problem. Today again, the latest methods of machine translation are trying to apply 'abstract representation' besides new findings of other methods to solve the problems of machine translation.              

This paper is an attempt to clarify the aims of MT and highlight some of the reasons why Persian MT is now a matter of necessity for Iranians in terms of its scientific, philosophical, social, and commercial benefits. After giving a short history of MT, I will illustrate that translation quality is not all that matters in the 21st century. I will show that there are some other factors involved that make MT an attraction for translators and non-translators the same. I will briefly review a few types of MT systems. From the rule-based approaches, transformer and linguistic knowledge (LK) architectures, and from the empirical approaches, example-based and statistical architectures are reviewed to give the reader an idea about how MT engines work. Linguistic and/or computational problems of each approach are discussed in short. It is argued that MT systems are better to use a combination of various architectures rather than sticking to a single approach. As an example of such architectures, Bond’s (2002) proposal for a Multi-Pass machine translation (MPMT) is reviewed. Wherever possible, technicality is avoided and references are made to the Persian language so that the material is more attractive and tangible for non-MT practitioners.

This paper tries to study some advantages and disadvantages of machine translation in comparison with human translation. It is believed that using computers in translation is not a threat for the livelihood of the human translator, but using machine translation as well as translation tools including translation memory can accelerate translation process and prevent translating repetitive, time-consuming and boring texts. Two criteria, need and use, have been considered to compare relative quality of machine translation and human translation. Each of these two kinds of translation can be selected according to the type of demand. machine translation with the goal of lowering linguistic barriers between nations can have a very effective role in dialogue among civilizations and communities having different languages and cultures.  

machine translation has a wide range of applications in the expansion of science and plays a crucial role as an interdisciplinary field in interconnecting science and technology.In this article, a historical survey of machine translation is presented together with some of its major strengths and weaknesses. Next, different methods of machine translation have been dealt with and the functions of language components in such systems are analyzed. Also, some systems are introduced in order to familiarize the reader with structural analysis software, and finally, semantics has been highlighted as a particularly important area in sophisticating the analysis of the language of origin, and as the major factor in improving the performance of systems of machine translation.

With the advent of the third millennium, communication has become the heart of human concern. In addition, the technological revolution has caused the necessity of convergent exchange of information regarding cultural and linguistic divergences. Therefore, no one can deny the significant role of a lingua franca or the translation as a bridge. Now with the huge amount of information exchange, no more do the traditional tools work. That's why machine translation (MT) or International Networks of translation should step into the scene. Although this necessity is undeniable, still there are some oppositions and criticisms. For sure human race cannot admit the substitution of their-own-made machines. It is true the human factor in machine translation is essential but is it fair to ignore the overall contribution of machine translations. Nevertheless, still we have the problem of "untranslatability" in some areas such as the translation of literary texts; machine translations have proved their out-performances in spelling and terminological consistency and their speed to facilitate the translation process. The inability of machine translation to refer to the real world knowledge is unavoidable. But is it possible to have robots exactly as human being? Wouldn't that be a creation? Hopefully we should expect improvements; but are human beings fair enough to judge the present amazing capabilities in machine translation?    

Background and Objectives: Although machine translation (MT) is extensively researched within the field of Artificial Intelligence (AI) and translation studies, few studies have attempted to implement MT output in foreign language teaching (FLT). One potential aspect of using MT in FLT refers to the implementation of MT output for reading comprehension. Considering the existing gap in the body of research on this issue, the present study aimed to investigate whether MT output is qualified enough to be used as an aid in EAP reading comprehension courses. More specifically, this study intended to benchmark the efficacy of MT output for EAP reading comprehension courses based on the data obtained from testing its comprehensibility and probing the students’ perceptions. To achieve the objectives of the study, MT was operationally defined as quality assessment in terms of output efficacy, a combination of usability and comprehensibility, which mirrors the ultimate goal of MT use in EAP reading comprehension courses, from the users' or target readers’ standpoint. Within this perspective, the current research was an attempt to assess the quality of MT output in terms of comprehensibility and the degree to which MT output might be comprehensible to the EAP students participating in this study.Materials and Methods: The participants of the study, 140 Iranian undergraduate university students majoring in the field of education at Farhangian University, Iran, were selected based on simple random sampling. Oxford Quick Placement Test was used to homogenize them in terms of English proficiency. Two versions of a reliable reading comprehension test, human translation (HT) and machine translation (MT), were given to. This test included 25 multiple-choice items, assessing the participants' literal comprehension of information stated in the passage as well as higher-order comprehension that required making inferences and conclusions. In particular, the items measured textual coherence, inference, reference, scanning, skimming, and word-meaning inference. To test the reliability of the tests, the KR-21 formula was applied and the results showed that both HT test (.83) and MT test (.78) were reliable. To investigate the perceptions of the participants on the efficacy of the MT output they encountered on the test, semi-structured interviews were conducted with some of the participants in Persian. Findings: With reference to the results of non-parametric tests such as Spearman’s rho, and Mann-Whitney Tests, and considering the observed effect sizes (Cohen’s d), it was revealed that, generally, the efficacy of MT output is comparable to that of HT. Moreover, in terms of reading comprehension sub-skills, the qualities of the two translations were comparable with regard to scanning, and inference, but not skimming and reference. Furthermore, the findings from the interview indicated that the students perceive MT to be a seminal aid for their EAP reading comprehension activities despite the minor problems that exist in the output such as morpho-syntactic errors or inappropriate lexical equivalents.Conclusions: The present study confirmed the fact that the efficacy of MT output is target-reader-dependent and text-dependent since it is determined both by the characteristics of the readers, such as their disciplines, and text features, as demonstrated by the significant differences in comprehension levels of the same readers measured by the same questions for HT and MT output. Accordingly, this study shed limelight on comprehensibility as a criterion of MT output efficacy; that is to say, it has to be reminded that MT quality needs to be defined as a context-bound and target-reader-specific concept.

Nowadays many people talk about machine translation and computers' potentials for this purpose. Due to lack of sufficient or proper understanding of computer power in this area, in this paper first a brief introduction of MT is given. The output of three MT systems dealing with Persian and English, namely Pars, Padideh and Google are then evaluated. It is not the aim of this paper to underestimate the efforts of individuals and companies producing these systems; rather, we are trying to show that machine translation is a huge undertaking and beyond the resources available to individuals and companies. Therefore, there is a long way ahead to attain systems capable of producing perfect translations.