Translation and Technology Essay Example for Free

Translation and Technology Essay Contents 4 Computer-Aided Translation Tools and Resources Workbenches Translation support tools and resources Localization tools Commercial computer-aided translation tools Standards for data interchange Conclusion 5 Evaluating Translation Tools Machine translation systems Computer-aided translation tools Stakeholders Evaluation methods General frameworks for evaluating translation tools Conclusion 6 Recent Developments and Future Directions Machine translation systems Computer-aided translation tools Translation systems with speech technology. Translation systems for minority languages Translation on the web Machine translation systems and the semantic web The localization industry Conclusion 7 Translation Types Revisited Relationships between topics and translation types Machine translation systems Computer-aided translation tools Conclusion Appendices References Index  93 93 106 113 117 119 128 129 129 131 133 135 139 151 152 152 156 157 162 164 166 170 171 172 173 191 193 195 197 204 218 List of Figures, Tables and Boxes Figures 1. 1 1. 2 1. 3 1. 4 1. 5 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 2. 9 2. 10 2. 11 2. 12 2. 13 3. 1 3. 2 3. 3 3. 4 3. 5 3. 6 3. 7 3. 8 3. 9 Classification of translation types Machine translation model Machine translation system based on usage Human-aided machine translation model Machine-aided human translation model Chronology of translation theories Translation process model Example of sentence representations Holmes’ schema of translation studies A schema of  applied translation studies A model of the translation process including pre- and post-editing tasks Example of an English SL text and its pre-edited version Unedited and post-edited Spanish machine translation output Example of natural and controlled languages. Example of original English text and its AECMA simplified English version Example of natural English, simplified English and simplified Arabic texts Example of an English controlled language text and its translations Illustration of the translation process using a machine translation system Chronology of machine translation development Example of structural representations. Machine translation architectures Direct translation model Interlingua model Interlingua multilingual machine translation system model Transfer model Transfer using tree-to-tree parsing Transfer multilingual machine translation system model ix  7 9 10 12 13 23 29 31 37 42 43 44 46 48 50 51 53 54 58 68 68 70 72 72 74 75 76 x List of Figures, Tables and Boxes 3. 10 3. 11 3. 12 3. 13 4. 1 4. 2 4. 3 4. 4 4. 5 4. 6 4. 7 4. 8 4. 9 4. 10 4. 11 4. 12 4. 13 4. 14 4. 15 4. 16 4. 17 4. 18 4. 19 4. 20 4. 21 4. 22 5. 1 5. 2 5. 3 5. 4 5. 5 6. 1 6. 2 Statistical-based model Probabilities workflow in the statistical-based approach Example-based model Translations by online machine translation systems Example of HTML code in a web page Example of the web page without HTML code Example of a translation workflow using a translation memory system Example of an English source text Pre-translation 1 Database model in translation memory systems Reference model in translation memory systems. Flowchart to illustrate how to build a parallel corpus Example of a text header in a corpus Example of part-of-speech tagging Example of a concordance for the word ‘round’ Types of tool used in a localization project Example of the translation process using a machine translation system, a translation database and a terminology database Example of TMX  data-sharing Example of a header in TMX Example of a body in TMX Example of a header in TBX Example of a body in TBX Example of XLIFF in the localization process Example of a header in XLIFF Example of a body in XLIFF Example of an alternate translation element in XLIFF Example of a glass-box evaluation. Example of a black-box evaluation Example of an evaluation process Standardization projects for evaluating machine translation systems EAGLES general evaluation framework Future-use model of translation technology Speech technology in translation. 78 80 81 87 99 99 102 102 103 103 104 109 110 111 112 114 117 120 121 122 124 125 126 127 127 127 138 139 141 142 145 154 158 Tables 1. 1 3. 1 An example of a table for describing translation types Example of a word entry in KAMI 8 67. List of Figures, Tables and Boxes  xi 3. 2 3. 3 3. 4 4. 1 4. 2 4. 3 4. 4 4. 5 4. 6 4. 7 4. 8 7. 1 7. 2 7. 3 7. 4 7. 5 7. 6 7. 7 7. 8 7. 9 7. 10 7. 11 7. 12 7. 13 7. 14 7. 15 7. 16 7. 17 7. 18 7. 19 7. 20 Imitation in the example-based approach Semantic similarity in the example-based approach Classification of commercial machine translation systems Example of perfect matching Examples of fuzzy matching Higher and lower threshold percentages for fuzzy matching Examples of matching suggestions for ‘bow’ Example of segments Example of translation units Example of English-French translation units from a database Classification of commercial computer-aided translation tools. Degree of automation Human intervention Integrated tools Application of theory Application of theory in machine translation systems Source-language texts Target-language texts Stages of the translation process Types of text Language dependency Types of source language Data interchange standards in translation Translation groups  and data interchange standards Levels of evaluation Methods of evaluation Features in a machine translation system Language coverage in machine translation systems Texts and computer-aided translation tools. Language dependency in computer-aided translation tools Number of languages in computer-aided translation tools. 82 82 88 95 96 97 98 100 101 102 118 174 175 175 176 177 178 180 181 182 185 186 187 188 189 190 191 193 194 194 195 Boxes 1. 1 5. 1 A translator at work FEMTI evaluation framework 14 147 Series Editors’ Preface Recent years have witnessed momentous changes in the study of Modern Languages, globally as well as nationally. On the one hand, the rapid growth of English as a universal lingua franca has rendered the command of other languages a less compelling commodity. On the other hand, the demand for intercultural mediators including translators and interpreters has grown as a result of many recent social, political and economic developments; these include legislative changes, the emergence of supranational organisations, the ease of travel, telecommunications, commercial pressures raising awareness of local needs, migration and employment mobility, and a heightened awareness of linguistic and human rights. Today, linguistically oriented students wishing to pursue a career in which they are able to further their interest in languages and cultures would be more inclined to choose vocationally relevant courses in which translation and interpreting play an important part rather than traditional. Modern Language degrees. Thus the possibilities for professional work in translation and interpreting have been extended, particularly as a result of developments in technology, whether as facilitating the translation process or as a means of dissemination and broadening access to communications in a range of media. The role of translation is, for example, becoming increasingly important in the context of modern media such as television and cinema, whether for documentary or entertainment purposes. And the technological possibilities for providing interpreting services, whether to the police officer on the beat or to the businessperson on a different continent, have extended the previously physically confined nature of mediating the spoken word. Not only do these new vistas open up opportunities for the professional linguist, they also point to expanding areas of research in Translation and Interpreting Studies. Practice and theory are of mutual benefit, especially in the case of a relatively young discipline such as Translation Studies. As a result, the first aim of this series, written primarily for the MA and advanced undergraduate student, is to highlight contemporary issues and concerns in order to provide informed, theoretically based, accounts of developments in translation and interpretation. The second aim is to provide ready access for students interested in the study and pursuit of Modern Languages to xii Series Editors’ Preface xiii vocational issues which are of relevance to the contemporary world of translating and interpreting. The final aim is to offer informed updates to practising professionals on recent developments in the field impacting on their discipline. Linguistic, Culture and Translation Studies University of Surrey Guildford UK GUNILLA ANDERMAN MARGARET ROGERS Acknowledgements I am indebted to three individuals for their contributions. This book would have taken more time to complete if it had not been for Chooi Tsien Yeo who researched background information for me. Words cannot express my gratitude to Stephen Moore, in between translation deadlines, for putting his experiences as a professional translator into writing. I am extremely indebted to Paul Marriott for his comments and suggestions, particularly on helping to visualize a new way to depict the multidimensional classification of translation types in Chapter 7. I would like to acknowledge especially the Duke University Libraries and Institute of Statistics and Decision Science at Duke University in providing me with the environment and research facilities where most of this book was written. Also my thanks to the National University of Singapore Libraries, George Edward Library at the University of Surrey, and the Department of Statistics and Actuarial Science at the University of Waterloo for their help. I would also like to acknowledge the following authors, publishers and organizations for allowing the use of copyright material in this book: John Hutchins, Harold Somers and Elsevier (Academic Press Ltd) for the classification of translation types in Chapter 1; Eugene Nida and the Linguistic Society of America for the translation process in Chapter 2; John Smart and Smart Communications, Inc. for the controlled and simplified English samples in Chapter 2; Francis Bond and Takefumi Yamazaki for the KAMI Malay–English dictionary entry in Chapter 3; Paolo Dongilli and Johann Gamper for the building of a parallel corpus in Chapter 4; Tony Jewtushenko and Peter Reynolds of OASIS for XLIFF in Chapter 4; Enrique de Argaez at Internet World Stats for the statistical figure on the Internet population in Chapter 6; Michael Carl, Reinhard Schaler, Andy Way, Springer Science and Business Media, and Kluwer Academic Publishers for the model of the future use of translation technology in Chapter 6. To Antonio Ribeiro, Tessadit Lagab, Margaret Rogers and Chooi Tsien Yeo, my most sincere thanks for translating from English into Portuguese, French, German and Chinese respectively. I am solely responsible for any translation errors that occurred. A special thank you goes to Elsie Lee, Shaun Yeo, Angeliki Petrits, Mirko Plitt and Ken Seng Tan for answering some of my queries. xiv Acknowledgements xv. To Caroline, Elizabeth, Gillian and Lyndsay, thank you for helping out with keying in corrections on the earlier drafts. Lastly, to my ‘sifu’ and friend Peter Newmark, a big thank-you for all the translation discussions we had during our coffee–biscuit sessions years ago. If it had not been for the series editors, Gunilla Anderman and Margaret Rogers, this book would not have been written. I am forever grateful to both of them for their feedback and comments. Thanks to Jill Lake of Palgrave Macmillan for her patience and understanding due to my ‘country-hopping’ from Southeast Asia to North America during the writing of this book. Waterloo, Canada CHIEW KIN QUAH List of Abbreviations. ACRoTERMITE AECMA AIA ALPAC ALPS ALT-J/C ALT-J/E ALT-J/M AMTA ASCC ASD ATA BASIC BLEU BSO CAT CAT2 CESTA CFE CIA CICC CRATER CTE CULT DARPA DBMT DIPLOMAT DLT DTS EAGLES EARS EDIG Terminology of Telecommunications European Association of Aerospace Industries Aerospace Industries Association of America Automatic Language Processing Advisory Committee Automatic Language Processing System Automatic Language Translator Japanese to Chinese Automatic Language Translator Japanese to English Automatic. Language Translator Japanese to Malay Association of Machine Translation in the Americas Automatic Spelling Checker Checker AeroSpace and Defence American Translators Association British American Scientific International, Commercial Bilingual Evaluation Understudy Buro voor Systeemontwikkeling Computer-Aided Translation Constructors, Atoms and Translators Campagne d’Evaluation de Systemes de Traduction Automatique Caterpillar Fundamental English Central Intelligence Agency Center of International Cooperation for Computerization Corpus Resources and Terminology Extraction Caterpillar Technical English Chinese University. Language Translator Defense Advanced Research Projects Agency Dialogue-based Machine Translation Distributed Intelligent Processing of Language for Operational Machine Aided Translation Distributed Language Translation Descriptive Translation Studies Expert Advisory Group on Language Engineering Standards Effective, Affordable Reusable Speech-to-Text European Defence Industries Group xvi List of Abbreviations xvii. ELDA ELRA ENGSPAN ENIAC EURODICAUTUM EUROSPACE EUROTRA EVALDA EWG FAHQT/FAHQMT FEMTI GENETER GETA HAMT HICATS HT HTML IAMT IATE INTERSECT ISI ISLE ISO JEIDA JEITA JICST-E KAMI KANT KGB LDC LISA LMT LTC LTRAC MAHT MANTRA MARTIF Evaluations and Language resources Distribution Agency European Language Resources Association English Spanish Machine Translation System Electronic Numerical Integrator and Computer. European Terminology Database Aerospace  and Defence Industries Association of Europe European Translation Infrastructure d’EVALuation a ELDA Evaluation Working Group Fully Automatic High Quality (Machine) Translation A Framework for the Evaluation of Machine Translation in ISLE Generic Model for Terminology Groupe d’Etude pour la Traduction Automatique Human-Aided/Assisted Machine Translation Hitachi Computer Aided Translation System Human Translation HyperText Markup Language International Association of Machine Translation Inter-Agency Terminology Exchange International Sample of English Contrastive. Texts International Statistical Institute International Standards for Language Engineering International Organization for Standardization Japan Electronic Industry Development Association Japan Electronics and Information Technology Association Japan Information Center of Science and Technology Kamus Melayu-Inggeris (Malay-English Dictionary) Knowledge-based Accurate Translation Komitet Gosudarstvennoi Bezopasnosti Linguistic Data Consortium Localisation Industry and Standards Association Logic-based Machine. Translation Language Technology Centre Language Translation Resources Automatic Console Machine-Aided/Assisted Human Translation Machine Assisted Translation Machine Readable Terminology Interchange Format xviii List of Abbreviations  MASTOR MAT METAL METU MLIR MT NAATI NIST OASIS OCP OCR OLIF OS OSCAR PaTrans PAHO PDA PESA RDF RFC SALT SGML SPANAM SUSY SYSTRAN TAP TAUM TBX TEMAA TGT-1 THETOS TMF TMX TOLL TONGUES TS TTS Multilingual Automatic Speech-to-Speech Translator Machine-Aided/Assisted. Translation Mechanical Translation and Analysis of Language Middle East Technical University MultiLingual Information Retrieval Machine Translation National Accreditation Authority for Translators and Interpreters Ltd. National Institute of Standards and Technology Organization for the Advancement of Structured Information Standards Oxford Concordance Programme Optical Character Recognition Open Lexicon Interchange Format Operating System Open Standards for Container/Content Allowing Re-use Patent. Translation Pan-American Health Organization Personal Digital Assistant Portuguese-English Sentence Alignment Resource Description Framework Request for Comments Standards-based Access to Lexicographical Terminological Multilingual Resources Standard Generalised Markup Language Spanish American Machine Translation System Saarbrucker UbersetzungsSYstem System Translation. Think-Aloud Protocols Traduction automatique a l’Universite de Montreal TermBase eXchange Testbed Study of Evaluation Methodologies: Authoring Aids Text-into-Gesture Translator Text into Sign Language Automatic Translator for Polish Terminological Markup Framework Translation Memory eXchange Thai On-Line Library Act II Audio Voice Translation Guide Systems Translation Studies Theoretical Translation Studies List of Abbreviations xix. WebDIPLOMAT WebOnt WWW W3C XLIFF XLT XML Web Distributed Intelligent Processing of Language for Operational Machine Aided Translation Web Ontology World Wide Web WWW Consortium XML Localisation Interchange File Format XML Representation of Lexicons and Terminologies Extensible or Extensive Markup Language. This page intentionally left blank Introduction For over half a century, the demand for a variety of translations by different groups of end-users has enabled many types of translation tools to be developed. This is reflected in the systems that will be discussed in this book, ranging from machine translation systems, computer-aided translation tools and translation resources. The majority of books and articles on translation technology focusing on the development of these systems and tools have been written from the point of view of researchers and developers. More recent publications written with translators in mind have focused on the use of particular tools. This book is intended as an introduction to translation technology for students of translation. It can also be useful to professional translators and those interested in knowing about translation technology. A different approach is taken in that descriptions of particular tools are not provided, and the development of different machine translation and computer-aided translation tools and their uses are discussed. Programming details and mathematical equations are not considered, except in the discussion of the statistical approach to machine translation where minimal essential formulae are included. Descriptions are given to allow readers to further investigate specific approaches or issues that might interest them, using references cited throughout the book. It is also important to note that no particular approach or design is deemed to be better than any other. Each and every one has their strengths and weaknesses. In many cases, readers will find that examples of systems and tools are given but this does not suggest that they are the best; they are simply examples to illustrate the points made. 1 2 Translation and Technology. While researching this book, I discovered that the majority of publications from the literature on translation technology are about the development of machine translation systems, primarily involving experimental systems developed or being developed at a number of universities and large commercial corporations across the globe. The book will show that many of these systems never achieved their commercial potential and remained as experimental tools, while some others served as tools for other natural-language processing applications. By contrast, not much literature seems to be available on computeraided tools such as translation memory systems. As we shall see in this book, most computer-aided translation tools are developed by commercial companies and, as a result, progress reports on these tools are rarely published in the public domain. Furthermore, to cater to different needs and demands, a tool like a translation memory system comes in many versions from the most basic to the most advanced. Insights into the use of these tools can be found in translator magazines and occasionally also posted on the World Wide Web (WWW). The evaluation of translation tools falls into a field that is wellresearched. Again we will see that most of the literature focuses on the evaluation of machine translation systems. Furthermore, the extensive use of translation tools and translation processes involved in the localization industry tend to be discussed separately, giving the impression that they are not related to translation. These two areas are, however, directly relevant to translation technology. Hence they are also included in this book. Essentially, the book contains what is felt should be included in order to provide an overview of translation technology. In order to keep the book at the given length, the topics have been carefully selected with some described in greater detail than others. In some chapters, an abbreviated historical background has been deemed necessary in order to provide a better understanding of the topics discussed, especially in the description of the development of machine translation systems and their evaluation. However, in all cases, references have been provided which readers may choose to pursue at a later time. Suggestions for further reading are provided at the end of every chapter (Chapters 1 to 6). The first chapter discusses the definitions of terms referring to the use of computers in translation activities. Some of the terms can be confusing to anyone who is unfamiliar with translation tools. In some cases, the same translation tools are given different names depending on what they are used for; in other cases, a tool may be differently classified depending on the perspective of those who have developed that tool. Introduction 3 The aim in this chapter is therefore to clarify these terminological and related matters. An alternative perspective to the four basic translation types – fully automated high-quality machine translation, human-aided machine translation, machine-aided human translation, and human translation – first proposed by Hutchins and Somers (1992) is introduced to reflect current developments in translation technology. This will be explored in more detail in the final chapter where the four translation types are reviewed in relation to topics described in the book. The second chapter discusses technology within the larger framework of Translation Studies as a discipline, focusing on the relationship between the engineering of translation technology, on the one hand, and Translation Studies including translation theory, on the other hand. The relationship between academic and professional groups involved in translation is also examined. This in turn leads to a discussion of the involvement of a particular approach in linguistic theories – known as ‘formalisms’ in natural-language processing – especially in the design of machine translation systems. A different perspective on the translation process involving pre- and post-editing tasks using a special variety of language called ‘controlled language’ is also presented. This translation process is described using the translation model proposed by Jakobson (1959/2000), a translation model that differs significantly from the one proposed by Nida (1969). The third chapter gives detailed descriptions of different machine translation system designs also known as ‘architectures’. The development of machine translation over several decades, its capabilities and the different types of machine translation systems, past and present, are also included. Both experimental and commercial systems are discussed, although the focus is on the experimental systems. Even though machine translation has been well-documented elsewhere, a discussion is deemed to be important for this book. It is felt that modern-day professional translators should be informed about machine translation systems because there is every reason to believe, as we shall discover in Chapter 6, that future trends in translation technology are moving towards integrated systems where at least one translation tool is combined with another, as is already the case in the integration of machine translation with translation memory. The fourth chapter describes the architectures and uses of several computer-aided translation tools, such as translation memory systems, as well as resources such as parallel corpora. Unlike machine translation systems, which are largely developed by universities, most computeraided translation tools are developed by commercial companies. Thus, 4 Translation and Technology information about such tools is harder to obtain. This chapter will also show that computer-aided translation tools are becoming more advanced and using different operating systems, and so ‘standards for data interchange’ have been created. Three different standards are described. Currently available commercial translation tools are also discussed. In addition, this chapter presents an overview of other commercially available tools such as those used in the localization industry. The fifth chapter touches on the evaluation of translation technology. The discussion focuses on different groups of stakeholders from research sponsors to end-users. Also included in the discussion are the different methods of evaluation: human, machine, and a combination of human and machine as evaluator. The choice of method used depends on who the evaluation is for and its purpose. It also depends on whether an entire tool or only some components are evaluated. Also described in this chapter is the general framework of evaluation offered by various research groups in the USA and Europe. The literature on evaluation concentrates on the evaluation of machine translation systems either during the developmental stage or after the process of development is completed. Less information is available on the evaluation of computeraided translation tools. What is available is found mainly in translation journals, magazines and newsletters. The sixth chapter presents some recent developments and shows the direction in which translation technology is heading, in particular regarding the future of machine translation systems that are now incorporating speech technology features. The integration of speech technology and traditional machine translation systems allows translation not only between texts or between stretches of speech, but also between text and speech. This integration is proving to be useful in many specific situations around the globe especially in international relations and trade. This chapter also looks at research projects in countries that are involved in the development of translation tools for minority languages and discusses the problems encountered in developing machine translation systems for languages that are less well-known and not widely spoken. Another form of technology called the ‘Semantic Web’ that has the potential to improve the performance of certain machine translation systems is also described. Included in this chapter, too, are issues such as linguistic dominance and translation demands on the WWW that are already shaping parts of the translation industry. The book concludes by presenting an expanded version of the four basic classifications of translation types as suggested by Hutchins and Somers (1992) and introduced in Chapter 1. It is concluded that the Introduction 5 one-dimensional linear continuum originally proposed is no longer able to accurately reflect current developments in translation technology. Translation tools today come in different versions and types depending on the purposes for which they are built. Some are multifunctional while others remain monofunctional. An alternative way must therefore be found to depict the complexities and multidimensional relationships between the four translation types and the topics discussed in this book. It is not possible to put every single subject discussed here into one diagram or figure, and so, in order to gain a better understanding of how the issues are related to one another, they are divided into groups. Topics or issues in each group have a common theme that links them together, and are presented in a series of tables. However, it is important to bear in mind that not all topics can be presented neatly and easily even in this way. This clearly shows the complexity and multidimensionality of translation activities in the modern technological world. At the end of the book, several Appendices provide information on the various Internet sites for many different translation tools and translation support tools such as monolingual, bilingual, trilingual and multilingual dictionaries, glossaries, thesauri and encyclopaedia. Only a selected few are listed here, and as a result the lists are not exhaustive. It is also important to note that some Internet sites may not be permanent; at the time of the writing, every effort has been made to ensure that all sites are accessible. 1 Definition of Terms In translation technology, terms commonly used to describe translation tools are as follows: †¢ †¢ †¢ †¢ †¢ †¢ machine translation (MT); machine-aided/assisted human translation (MAHT); human-aided/assisted machine translation (HAMT); computer-aided/assisted translation (CAT); machine-aided/assisted translation (MAT); fully automatic high-quality (machine) translation (FAHQT/FAHQMT). Distinctions between some of these terms are not always clear. For example, computer-aided translation (CAT) is often the term used in Translation Studies (TS) and the localization industry (see the second part of this chapter), while the software community which develops this type of tool prefers to call it ‘machine-aided translation’ (MAT). As the more familiar term among professional translators and in the field of Translation Studies, ‘computer-aided translation’ is used throughout the book to represent both computer-aided translation and machine-aided translation tools, and the term ‘aided’ is chosen instead of ‘assisted’, as also in ‘human-aided machine translation’ and ‘machine-aided human translation’. Figure 1.  1 distinguishes four types of translation relating human and machine involvement in a classification along a linear continuum introduced by Hutchins and Somers (1992: 148). This classification, now more than a decade old, will become harder to sustain as more tools become multifunctional, as we shall see in Chapters 3, 4 and 6. Nevertheless, the concept in Figure 1. 1 remains useful as a point of reference for classifying translation in relation to technology. 6 Definition of Terms 7 MT CAT Machine Fully automated high quality (machine) translation (FAHQT/ FAHQMT) Human-aided machine translation (HAMT) Machine-aided human translation (MAHT) Human Human translation (HT) MT = machine translation; CAT = computer-aided translation Figure 1. 1 Source: Classification of translation types Hutchins and Somers (1992): 148. The initial goal of machine translation was to build a fully automatic high-quality machine translation that did not require any human intervention. At a 1952 conference, however, Bar-Hillel reported that building a fully automatic translation system was unrealistic and years later still remained convinced that a fully automatic high-quality machine translation system was essentially unattainable (Bar-Hillel 1960/2003: 45). Instead, what has emerged in its place is machine translation, placed between FAHQT and HAMT on the continuum of Figure 1. 1. The main aim of machine translation is still to generate translation automatically, but it is no longer required that the output quality is high, rather that it is fit-for-purpose (see Chapters 2 and 3). As for human-aided machine translation and machine-aided human translation, the boundary between these two areas is especially unclear. Both classes are considered to be computer-aided translation as indicated in Figure 1. 1 (Tong 1994: 4,730; see also Slocum 1988; Hutchins and Somers 1992). However, in Schadek and Moses (2001), a different classification has been proposed where only machine-aided human translation is viewed as synonymous with computer-aided translation. Human-aided machine translation is considered as a separate category. The reasoning behind the view offered by Schadek and Moses is not difficult to understand. At least theoretically, the difference between the two is obvious. For human-aided machine translation, the machine is the principal translator, while in machine-aided human translation it is a human. In practice, however, it may be less easy today to draw a distinguishable boundary between them. The blurring of boundaries is further complicated when human-aided machine translation is considered as a subclass of machine translation, an approach chosen by Chellamuthu (2002). Since human-aided machine translation has 8 Translation and Technology the machine as the principal translator –

Digital Home Convergence Essay -- Technology Technological Essays

Digital Home Convergence Overview As technology and entertainment converge inside the home, major players in various industries are taking different strategies in planning for the future. The concept of the â€Å"digital home† will likely take one of two forms: a closed wired entertainment network (PVRs, OnDemand) or an open wireless data network (web, email, VOIP, IPTV). The closed model is already being offered in many homes across the country, but as technology advances, the open model will become the standard. It is important to understand how both technology and entertainment are companies are strategizing to control the â€Å"digital home†. Based on research in this report, it appears that four things are likely to occur. First, convergence of technology will create the need for standards in the digital home network. Much in the way it took some time before the Wintel model became standard, we will have to wait and see which model takes hold in home entertainment. Once a model is chosen as standard, there will be an explosion of products and services catering to that model in the market. Second, increasing competition will lead to alliances between companies in different industries. PC technology companies that have no presence in home entertainment might look to deal their way into the â€Å"digital home†. For example, Microsoft is currently striking deals with several major phone companies to provide the platform for IPTV. Many people also suspect that Apple might acquire TiVo in order to gain a presence in TV and create a service platform for video-on-demand. Third, the successful companies in the battle for t he digital home will be the ones who earn customer loyalty. Consumers are reluctant to change their behaviors, especially ones like TV watching that are so entrenched. Companies need to deliver products and services reliably in order to facilitate a consumer shift to the â€Å"digital home†. Finally, major content providers will hold up the process until they see digital distribution as a way to increase their long-term profits. Home video divisions of entertainment companies are extremely profitable. They do not want to jeopardize this profitability just to be at the front of the technology curve. The infrastructure behind the â€Å"digital home† will have to be in place before the major studios choose to participate. Hardware - PCs Companies in the ... ...cess is due to a combination of factors, including â€Å"pricing, infrastructure, demographics, geography, deregulation, and clear user benefits.† Conclusion In conclusion, we expect to observe significant convergence of technology and entertainment, which will either be in the form of a closed wired entertainment network (PVRs, OnDemand) or an open wireless data network (web, email, VOIP, IPTV). We predict that the open model will become a standard in the future. In support of our thesis, we would like to recapitulate four major trends that will likely happen in the near-term future. 1. A standard(s) will be created in the digital home network. Once a model is chosen as a standard, we can expect to see a proliferation of products and services that are compatible with that standard. 2. Increasing competition will lead to increased synergies and alliances amongst hardware, software and distribution companies. 3. The companies successful in the digital home entertainment space will be the ones who earn customer loyalty. 4. Major content providers (e.g. movie studios) will hold up the process until they see digital distribution as a way to increase their long-term profits.

“Good Country people” and “Where are you going, Where have you been?” Essay

There are many similarities between the short stories â€Å"Good Country People† and â€Å"Where are you going, Where have you been?†, most notably their characters. Both stories contain a female protagonist, and a male antagonist, whose confrontations start out relatively normal, and progress to more and more surreal and twisted endings. Their main characters, Hulga and Connie, are shockingly similar, and yet strangely different, one a 15 year old wishing to be older and beautiful, the other a bitter 32 year old, wishing to be younger and ugly. These stories tell the tales of impressionable young women who are tempted by the delights of strange men, only to prove to themselves in the end how naive they really are. In â€Å"Where are you going, Where have you been?†, Connie starts out as most teenage girls seemingly would – she wants to be more daring, to appear older, to experience more of the world. She sneaks away from childish pursuits, to the teenage or adult world, to drink and kiss boys rather than shop for school clothes, to see movies in a steamy car instead of in a theater. She talks of being beautiful as if it were her only good grace – beauty, to her, is the ultimate goal. She wants to be older, and more beautiful, and this is her downfall. Her foolishness, and her naivety is what appeals to Arnold Friend in the first place. Arnold Friend, a stranger, appeals to her early on in the story. He is older, more powerful, and smarter. She is frightened, of course, but intrigued, and it is her yearning for the adult world, and the adult life, that, in the end, causes her downfall. She is suckered in by the convincing conman who uses his words to appeal to her weaknesses. She is tricked into being what Arnold wants her to be by his smooth words and his faà §ade of confidence. She’s toyed with, played for the naà ¯ve fool she is, who is far too young for the world she wants to be a part of. Only at the very end of the story does she begin to realize what she has gotten herself into. She shows her true colors once she is confronted. In â€Å"Good Country People†, Joy is a relatively normal girl with some not-too-normal problems. For one thing, her leg got blown off when she was younger in a bizarre hunting accident. This physical change made her completely self conscious, and essentially ruined her life. She could no  longer be happy being herself, because she sees herself as true ugliness now. Thus, she feels forced to make herself what she thinks she is. She hates beauty now, and changes everything about her to seem ugly. She’s been to college, and yet still acts childish. She’s trying to be young, and ugly. And Manley Pointer notices this quality of her, and takes advantage of her. No matter how ugly she tries to be, he still tries to (or at least pretends to) like her for who she is. Hulga is, regardless of her ugly campaign, extremely flattered, and lets her guard down long enough for Manley to get away with her glasses, her leg, and more importantly, her dignity. She is also played f or a fool based completely on her own insecurities. She too is a victim of a conman who notices that things aren’t always what they seem. Connie and Hulga are very similar, as characters, and yet very different all the same. They both have their insecurities, and they are both easily preyed on by conmen and smooth talkers, but their insecurities are in entirely different realms. They both want what the other has, and due to this, they are constantly trying to be someone else, not themselves, and this is what makes them so easy to attack. They don’t know who they really are, and they think they want to be something else. This naivety is their downfall – they pretend to be something else, join a group they shouldn’t be in, and they are tempted by the men in these groups. But, when the tables turn, and their men aren’t what they appear to be, Connie and Hulga revert completely, from relatively confident phonies to sniveling little girls, helpless and hopeless, in their fake lives. These two women are seemingly innocent, random bystanders picked by older smarter conmen. However, one could easily hold them responsible for their own fates. Not that the victim in a crime is to blame, but, honestly, if you leave your car door open, with the keys inside, and the motor running, while you go inside a store for a few hours, how can you possibly seem shocked when it gets stolen? These two women, whether they believe it or not, are waving hundreds of flags at these conmen – â€Å"Please target me!†Ã¢â‚¬ ¦ â€Å"Take my leg!†Ã¢â‚¬ ¦ By openly flaunting their insecurities and by allowing themselves to be charmed to the point of trusting the conmen, they are, if not wholly, then at least partially responsible for their own fates. They reached their  own conclusions, and they got what they deserved. Connie and Hulga are the same person, essentially – a woman with different problems wishes to be something that they are not, and wiser and smoother conmen see this, and take advantage of them. In the end, they are proven to be the phonies that they really are, and are left more vulnerable, and more open, than they were before they tried to infiltrate the world in which they didn’t belong. If there were a shared moral to these stories, and there is most definitely not an obvious one, they’d both be somewhere along the lines of â€Å"Be happy with what you have, because you might not belong anywhere else†, and in the cases of Connie and Hulga, this moral fits perfectly. They are the same person with different circumstances, and they are so easily preyed on by the wiser smoother conman. As these stories blatantly state, be happy with what you have. You might not fit anywhere else, and one day, someone might just call you on your bluff, to disastrous conseque nces.

Poner Conjugation in Spanish, Translation, Examples

The conjugation of the Spanish verb poner, often translated as to put or to place, is highly irregular. In order to help you understand and use this verb, this article includes poner conjugations in the present, past, conditional, and future indicative; the present and past subjunctive; the imperative, and other verb forms. The same conjugation pattern is used for other verbs based on poner, such as componer, disponer, exponer, imponer, oponer, proponer, reponer and suponer. Using the Verb Poner vs. Ponerse The verb poner generally means to put or to place, but its meaning can vary when used in some common expressions like poner la mesa (to set the table), or poner huevos (to lay eggs). It can also mean to turn on, as in poner mà ºsica (to play music on the radio) or poner la televisià ³n (to turn the television on). The verb poner can also be used reflexively —ponerse—. Ponerse can mean to put something on, such as clothing or accessories. For example, Juan se puso el abrigo y Ana se puso el sombrero (Juan put the coat on and Ana put the hat on). In addition, ponerse can mean become when referring to a change in state of being, such as ponerse triste (become sad), ponerse rojo (to become red in the face), ponerse flaco (to become skinny), etc. Poner Present Indicative In the present indicative tense, the first person singular (yo) conjugation of the verb poner is irregular, but the rest of the conjugations follow a regular verb pattern. Yo pongo I put Yo pongo la mesa antes de la cena. Tà º pones You put Tà º pones el libro en la biblioteca. Usted/à ©l/ella pone You/he/she puts Ella pone flores para decorar la casa. Nosotros ponemos We put Nosotros ponemos el dinero en el banco. Vosotros ponà ©is You put Vosotros ponà ©is la ropa en el armario. Ustedes/ellos/ellas ponen You/they put Ellos ponen mucho esfuerzo en su trabajo. Poner Preterite Indicative The preterite tense conjugations of poner are irregular and use the stem pus-. Yo puse I put Yo pusela mesa antes de la cena. Tà º pusiste You put Tà º pusisteel libro en la biblioteca. Usted/à ©l/ella puso You/he/she put Ella pusoflores para decorar la casa. Nosotros pusimos We put Nosotros pusimosel dinero en el banco. Vosotros pusisteis You put Vosotros pusisteis la ropa en el armario. Ustedes/ellos/ellas pusieron You/they put Ellos pusieronmucho esfuerzo en su trabajo. Poner Imperfect Indicative The verb poner is conjugated regularly in the imperfect tense. You start with the stem pon and add the imperfect ending for -er verbs (à ­a, à ­as, à ­a, à ­amos, à ­ais, à ­an). The imperfect tense can be translated as was putting or used to put. Yo ponà ­a I used to put Yo ponà ­ala mesa antes de la cena. Tà º ponà ­as You used to put Tà º ponà ­asel libro en la biblioteca. Usted/à ©l/ella ponà ­a You/he/she used to put Ella ponà ­a flores para decorar la casa. Nosotros ponà ­amos We used to put Nosotros ponà ­amosel dinero en el banco. Vosotros ponà ­ais You used to put Vosotros ponà ­aisla ropa en el armario. Ustedes/ellos/ellas ponà ­an You/they used to put Ellos ponà ­anmucho esfuerzo en su trabajo. Poner Future Indicative For the irregular conjugation of poner in the future indicative, change the stem to pondr-. Yo pondrà © I will put Yo pondrà © la mesa antes de la cena. Tà º pondrà ¡s Youwill put Tà º pondrà ¡s el libro en la biblioteca. Usted/à ©l/ella pondrà ¡ You/he/shewill put Ella pondrà ¡ flores para decorar la casa. Nosotros pondremos Wewill put Nosotros pondremos el dinero en el banco. Vosotros pondrà ©is Youwill put Vosotros pondrà ©isla ropa en el armario. Ustedes/ellos/ellas pondrà ¡n You/theywill put Ellos pondrà ¡n mucho esfuerzo en su trabajo. Poner PeriphrasticFuture Indicative The periphrastic future is composed of the present indicative conjugation of the verb ir (to go), the preposition a, and the infinitive poner. Yo voy a poner I am going to put Yo voy a ponerla mesa antes de la cena. Tà º vasa poner You aregoing to put Tà º vasa poner el libro en la biblioteca. Usted/à ©l/ella vaa poner You/he/shegoing to put Ella vaa poner flores para decorar la casa. Nosotros vamosa poner We aregoing to put Nosotros vamos a poner el dinero en el banco. Vosotros vaisa poner You aregoing to put Vosotros vaisa poner la ropa en el armario. Ustedes/ellos/ellas vana poner You/they aregoing to put Ellos vana poner mucho esfuerzo en su trabajo. Poner Present Progressive/Gerund Form To form the gerund  or present participle, you start with the stem of the verb and then add the ending -ando (for -ar verbs) or -iendo (for -er and -ir verbs). The present participle is used to form progressive tenses like the present progressive, which is usually formed with the auxiliary verb estar, but can also use the verbs seguir, continuar or mantener as the auxiliary. Present Progressive ofPoner està ¡ poniendo is putting Ella està ¡ poniendo flores para decorar la casa. Poner Past Participle The past participle of poner is irregular —puesto—. This verb form can be used to form perfect tenses, such as the present perfect (with the auxiliary verb haber). Present Perfect of Poner ha puesto has put Ella ha puesto flores para decorar la casa. Poner Conditional Indicative To talk about possibilities, you can use the conditional tense, which is usually translated to English as would verb. Poner is also irregular in the conditional and uses the stem pondr-. Yo pondrà ­a I would put Yo pondrà ­ala mesa antes de la cena si llegara a tiempo. Tà º pondrà ­as Youwould put Tà º pondrà ­as el libro en la biblioteca si hubiera espacio. Usted/à ©l/ella pondrà ­a You/he/shewould put Ella pondrà ­a flores para decorar la casa, pero las flores està ¡n muy caras. Nosotros pondrà ­amos Wewould put Nosotros pondrà ­amos el dinero en el banco si nos ganà ¡ramos la loterà ­a. Vosotros pondrà ­ais Youwould put Vosotros pondrà ­ais la ropa en el armario si fuerais mà ¡s ordenados. Ustedes/ellos/ellas pondrà ­an You/theywould put Ellos pondrà ­an mucho esfuerzo en su trabajo, pero son perezosos. Poner Present Subjunctive The present subjunctive is formed with the stem of the first person singular in the present indicative (yo pongo). Que yo ponga That I put Mamà ¡ pide que yo ponga la mesa antes de la cena. Que tà º pongas That you put El maestro quiere que tà º pongas el libro en la biblioteca. Que usted/à ©l/ella ponga That you/he/she put La decoradora recomienda que ella ponga flores para decorar la casa. Que nosotros pongamos That we put El contador sugiere que nosotros pongamos el dinero en el banco. Que vosotros pongà ¡is That you put Papà ¡ pide que vosotros pongà ¡is la ropa en el armario. Que ustedes/ellos/ellas pongan That you/they put La jefa espera que ellos pongan mucho esfuerzo en su trabajo. Poner Imperfect Subjunctive The imperfect subjunctive has two different conjugations. Both of them are correct. Option 1 Que yo pusiera That I put Mamà ¡ pedà ­a que yo pusiera la mesa antes de la cena. Que tà º pusieras That you put El maestro sugerà ­a que tà º pusieras el libro en la biblioteca. Que usted/à ©l/ella pusiera That you/he/she put La decoradora recomendaba que ella pusiera flores para decorar la casa. Que nosotros pusià ©ramos That we put El contador sugerà ­a que nosotros pusià ©ramos el dinero en el banco. Que vosotros pusierais That you put Papà ¡ pedà ­a que vosotros pusierais la ropa en el armario. Que ustedes/ellos/ellas pusieran That you/they put La jefa esperaba que ellos pusieran mucho esfuerzo en su trabajo. Option 2 Que yo pusiese That I put Mamà ¡ pedà ­a que yo pusiese la mesa antes de la cena. Que tà º pusieses That you put El maestro sugerà ­a que tà º pusieses el libro en la biblioteca. Que usted/à ©l/ella pusiese That you/he/she put La decoradora recomendaba que ella pusiese flores para decorar la casa. Que nosotros pusià ©semos That we put El contador sugerà ­a que nosotros pusià ©semosel dinero en el banco. Que vosotros pusieseis That you put Papà ¡ pedà ­a que vosotros pusieseis la ropa en el armario. Que ustedes/ellos/ellas pusiesen That you/they put La jefa esperaba que ellos pusiesen mucho esfuerzo en su trabajo. Poner Imperative The imperative mood is used to give orders or commands. Positive Commands Tà º pon Put!  ¡Pon el libro en la biblioteca! Usted ponga Put!  ¡Ponga flores para decorar la casa! Nosotros pongamos Let's put!  ¡Pongamos el dinero en el banco! Vosotros poned Put!  ¡Poned la ropa en el armario! Ustedes pongan Put!  ¡Pongan mucho esfuerzo en su trabajo! Negative Commands Tà º no pongas Don't put!  ¡No pongas el libro en la biblioteca! Usted no ponga Don't put!  ¡No ponga flores para decorar la casa! Nosotros no pongamos Let's not put!  ¡No pongamos el dinero en el banco! Vosotros no pongà ¡is Don't put!  ¡No pongà ¡is la ropa en el armario! Ustedes no pongan Don't put!  ¡No pongan mucho esfuerzo en su trabajo!

Computer Viruses A Big Problem For The Average Computer...

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Implementing Change Kotter s 8 Step Approach - 1850 Words

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Legacy Systems Management

Question: Write an Eassay on Legacy Systems? Answer: Introduction The old technologies, method, application system or computer system is known as legacy system in the computing world. The legacy systems that are still in use are in a dire need of replacement. There are many organizations that still use these legacy systems. These legacy systems are still in use because it is been in use since the earliest period of time and the organizations are hesitant to change them completely. The organizations are hesitant because of the fact that if the whole system is changed than how will the organizations keep running till the new systems are installed (Aciworldwide.com, 2015). Many new systems or computer programs are developed these days, they are far more easy and flexible in nature. The computer languages that are in huge demand and are being used in many organizations are: c, c++, java and unix. Still the use of legacy system is present in many organizations. Significance Of Legacy Systems Organizations spend a huge amount of money to install software systems to get the desired results. They spend the money thinking that the software will be in use for many years. Market is changing constantly in a fast pace ad with it the software are being updated as well as many new software are being introduced in the market. Still the companies tend to stick with the software that they have been using for a long time. Organizations still rely on the old systems because these systems play a very vital role in the organizations services or products. Many old systems have become very vital in many businesses because they perform their daily work using these legacy systems. Situation might arise where the organization will not be able to function if these systems collapse (Aciworldwide.com, 2015). A constant change is taking place in each part of the market and these legacy systems have incorporated all these changes over the years. It can be said in a way that these systems have a lo t of information in them and changing it would be a very difficult task. Organizations change or replace their machines and systems frequently to match with the change in market, but changing these legacy systems might be a little risky. Changing the systems to incorporate new and latest systems might produce results that are not correct or may not perform up to the mark as it used to do earlier. Advantages Of Legacy Systems Using legacy systems could cost the organization more than using a new system, but there are many advantages of using these legacy systems (Aciworldwide.com, 2015). Some of the advantages of Legacy systems are as follows: Risks related to installing new systems that have not used before will be reduced. This is so because the organization knows how to work with the present system and know the results that are generated from using the legacy systems. The organization will not have to spend any money, thus cost to install a new system will not come to play. With the changing market the legacy system have also incorporated these changes in them and by doing so they have a lot of vital information present in them. They have information about the company from the earliest of time and thus all these data can be easily accessed. Installing a new software will complicate accessing data and the method of working. Modernizing Legacy Systems The legacy systems can be completely replaced or updated. There may be a lot of challenges in modernizing legacy system that is present in any organization. A legacy system can be modernized to meet the new demands in the market. With passing time new and fresh software are launched that are more flexible, less complicated, structured and less costly. The legacy systems can be replaced with any of the software that is close enough to the requirement of the company, but replacing the whole system may come with many risks (Aciworldwide.com, 2015). The legacy system can be updated, updating the system will be a better option as the whole system will not be changed and the functionality will remain same. Updating legacy system will be helpful for the company. The company then will not have to appoint new employees or expert who knows how to work with the new software. There will be no change in platform and thus the result obtained will be consistent. Conclusion A legacy system has its own set of advantages and disadvantages. Companies or organization have to change their working method with the change in the market. To stay ahead in the competition the systems must be modernized to a length where there are no risks or damage to the organization. Reference com,. (2015).Managing complexity - living with legacy systems. Retrieved 25 May 2015, from https://www.aciworldwide.com/en/News-and-events/ACI-in-the-news/111202-Managing-complexity-living-with-legacy-systems.aspx Libby, S., Van Bibber, K. (2010).Edward Teller Centennial Symposium. Hackensack, NJ: World Scientific. Ulrich, W., Newcomb, P. (2010).Information systems transformation. Amsterdam: Morgan Kaufmann Object Management Group/Elsevier. Young, D., McCarthy, S. (1999).Managing integrated delivery systems. Washington, D.C.: AUPHA Press.