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.

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.

There are various centers working on the filed of Machine Translation in Iran but so far only two of them have produced commercial applications available in the market. The aim of this paper is to evaluate these two applications and comment on their advantages and weaknesses. In order to achieve this goal, we first review the general principles covering the evaluation of the output generated by (MT) applications; then we will have to proceed with our own evaluation.

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 (MP(MT)) 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 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 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.  

Today technology is an integral part of professional Translation; and it is generally assumed that translators’ attitudes toward Translation technology tools influence their interaction with technology (Bundgaard, 2017). Therefore, the present twophase study seeks to shed some light on what Translation technology tools are and how professional translators feel toward them. The research method used is exploratory in nature, as it tends to discuss issues on which little research has been done and relies on secondary research for its data. The data required for answering the first question have been mined utilizing document analysis from language service providers’ (LSPs) websites, while the data for working out the answer to the second question have been obtained from ProZ. com Quick Polls. Based on our findings, Translation technology tools fall into eight broad categories, of which the most commonly used are Translation memory (TM) or computer-assisted Translation (CAT) tools. In addition, it was found that most translators either do not have a love-hate relationship with technology or love it. This research is envisaged to form the basis of more detailed and conclusive studies.

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.