Technology for Translators: What doesn’t Kill you, Makes you Stronger (original) (raw)
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Some approaches for a machine translation evaluation system (MTES)
Today Machine Translation (MT) systems are commercially available for a variety of language pairs and in price range, which makes them accessible to the nonprofessionals. Yet there is no standard evaluation for any type of translation systems whether automatic or manual especially for the commercial systems that involve Arabic language in the Arabic region. This paper presents a brief survey of evaluation of MT system methods and its importance. It also presents some approaches for developing a comprehensive evaluation system without any developer cooperation. Although we proposed some dimension for MTS's, we concentrated on translation quality evaluation of the MTS not the MTS itself dimension for MTS's, we concentrated on translation quality evaluation of the MTS not the MTS itself. dimension for MTS's, we concentrated on translation quality evaluation of the MTS not the MTS itself 2 The situation in the Arabic region As the globalization of the Arabic world becomes common, more than 180 million speakers around the world, problems caused by lack of communication can seriously affect its situation especially as a receiver of knowledge more than a producer. Thereby its need to MT is essential and there should 1 Some approach for a machine
2001
This paper describes a Machine Translation (MT) evaluation experiment where emphasis is placed on the quality of output and the extent to which it is geared to different users' needs. Adopting a very specific scenario, that of a multilingual international organisation, a clear distinction is made between two user classes: translators and administrators. Whereas the first group requires MT output to be accurate and of good post-editable quality in order to produce a polished translation, the second group primarily needs informative data for carrying out other, non-linguistic tasks, and therefore uses MT more as an information-gathering and gisting tool. During the experiment, MT output of three different systems is compared in order to establish which MT system best serves the organisation's multilingual communication and information needs. This is a comparative usability- and adequacy-oriented evaluation in that it attempts to help such organisations decide which system prod...
Evaluation of machine translation systems and related procedures
ARPN journal of engineering and applied sciences, 2018
Currently, the high volume of international information exchange involves a wide range of localities. As each locality comes with its own distinctive dialect, the need for an effective means of language translation is becoming more and more apparent. Among the concerns of information professionals is the capacity of an interested party to access web information offered in an unfamiliar language. Classified under the wide field of artificial intelligence, machine translation (MT) is an approach related to natural language processing. The machine translation technique involves the use of software for the conversion of documents or verbalized information from one natural language into another. Of late, a substantial number of procedures have been proposed for the fashioning of an efficient MT system. While these procedures were observed to be capable in certain areas, they were found wanting in others. The objectives of this endeavour are to (a) conduct a thorough investigation on mach...
Evaluating the Translation Accuracy of a Novel Language-Independent MT Methodology
The current paper evaluates the performance of the PRESEMT methodology, which facilitates the creation of machine translation (MT) systems for different language pairs. This methodology aims to develop a hybrid MT system that extracts translation information from large, predominantly monolingual corpora, using pattern recognition techniques. PRESEMT has been designed to have the lowest possible requirements on specialised resources and tools, given that for many languages (especially less widely used ones) only limited linguistic resources are available. In PRESEMT, the main translation process is divided into two phases, the first determining the overall structure of a target language (TL) sentence, and the second disambiguating between alternative translations for words or phrases and establishing local word order. This paper describes the latest version of the system and evaluates its translation accuracy, while also benchmarking the PRESEMT performance by comparing it with other established MT systems using objective measures.
Computer-assisted translation. Its advantages and disadvantages
2016
Kyiv national university of technologies and design, Kyiv Translation has undergone several stages in its development, but currently, the preference is given to informative translation in which the features of individual author's style are not so important. The development of information technology has resulted in computer programs to facilitate translation; we should know the advantages and disadvantages of this type of translation. Translation is a complex multifaceted phenomenon, some aspects of which may be the subject of study of different sciences. In the framework of translation studies one examines psychological, literary, ethnographic and other spheres of translation practice, as well as history of translation practice in a particular country or countries. But the main topic of my paper will be computer-assisted translation or computer-aided translation. And now it is important to make a distinction between machine translation (MT) and computer-assisted translation (CAT). On a schematic level, machine translation involves the calculation speed of a computer in order to analyse the structure of each term or phrase within the text to be translated (source text). It then breaks this structure down into elements that can be easily translated, and recomposes a term of the same structure in the target language. In doing so, the method calls upon the use of highly voluminous, multilingual dictionaries plus sections of text that have already been translated [2]. Using a computer-assisted translation tool is a process which includes the use of software to aid individuals in translating. In case of time constraints, a computer-assisted translation tool can effectively reduce the translation time, enabling the translator to translate content in a timely manner [5]. The major distinction between MT and CAT lies with who is a primarily responsible for the actual task of translation. In MT, the computer translates the text, though the machine outputs may later be edited by a human translator. In CAT, translators are responsible for doing the translation, but they may make use of a variety of computerized tools to help complete this task and increase their productivity. Therefore, whereas MT systems try to replace translators, CAT tools support translators by helping them to work more efficiently [1, p. 4]. Humans and computers each have their strengths and weaknesses. The idea of CAT software is to make the most of the strengths of people and computers. Translation performed solely by computers has very poor quality. Meanwhile, no human can translate as fast as computer can. If we accept that translation demands total sensitivity to the cognitive aspects of a source text, it follows that a computer would need to understand language and assimilate facts in the way that humans do it in order to resolve textual ambiguity and create a version that paid due regard to semantic content and register. For example, an awareness of context is essential for the correct interpretation of a sentence such as visiting European dignitaries can be a nuisance. In translating this sentence, a human translator would take into account the sentences which preceded and followed, as well as the general context, the overall theme of the text and any relevant social, economic or cultural factors. However, a computers inability to acquire, comprehend and rationally apply real-world knowledge in this way does not render MT useless as a production tool. Raw MT output does not need to be perfect in order to be useful. Direct comparisons between a system's raw output and human translation are pointless; as MT is a production tool, its capacity to increase or speed up production, within acceptable cost parameters, is the only valid measure of its effectiveness. If its use can be shown to increase productivity and reduce costs, it is clearly advantageous; if it fails to de either, it is a white elephant [3, pp. 3-4]. By using a CAT tool, however, you can gain some of the speed, consistency and memory
Modern MT Systems and the Myth of Human Translation: Real World Status Quo
This paper objects to the current consensus that machine translation (MT) systems are generally inferior to human translation (HT) in terms of translation quality. In our opinion, this belief is erroneous for many reasons, the both most important being a lack of formalism in comparison methods and a certain supineness to recover from past experience. As a side effect, this paper will provide evidence for a much more favorable judgment of the performance of contemporary MT systems. We will present and discuss known methods of automatic MT evaluation, give real world examples of both machine and human translation and finally suggest an universal formal evaluation method to handle both human, as well as MT output in a comparable fashion.
Proceedings of Translating and the Computer 44, 2023
The author conducted an anonymous online survey between 23 July and 21 October 2022 to gain insight into the proportion of translators that use machine translation (MT) in their translation workflow and the various ways they do. The results show that translators with more experience are less likely to accept MT post-editing (MTPE) assignments than their less experienced colleagues but are equally likely to use MT themselves in their translation work. Translators who deal with lower-resource languages are also less likely to accept MTPE jobs, but there is no such relationship regarding the use of MT in their own workflow. When left to their own devices, only 18.57% of the 69.54% of respondents that declared that they use MT while translating always or usually use it in the way the pioneers of MT envisaged, i.e., MTPE. Most either usually or always prefer to use MT in a whole range of other ways, including enabling MT functions in CAT tools and doing hybrid post-editing; using MT engines as if they were dictionaries; and using MT for inspiration. The vast majority of MT-users see MT as just another tool that their clients do not necessarily need to be informed about.
Methodology for the Evaluation of Machine Translation Quality
Translation Studies: Theory and Practice
Along with the development and widespread dissemination of translation by artificial intelligence, it is becoming increasingly important to continuously evaluate and improve its quality and to use it as a tool for the modern translator. In our research, we compared five sentences translated from Armenian into Russian and English by Google Translator, Yandex Translator and two models of the translation system of the Armenian company Avromic to find out how effective these translation systems are when working in Armenian. It was necessary to find out how effective it would be to use them as a translation tool and in the learning process by further editing the translation. As there is currently no comprehensive and successful method of human metrics for machine translation, we have developed our own evaluation method and criteria by studying the world's most well-known methods of evaluation for automatic translation. We have used the post-editorial distance evaluation criterion as ...
For a long time, machine translation and professional translation vendors have had a contentious relation. However, new tools, computing platforms, and business models are changing the fundamentals of this relationship. I will review the main trends in the area while emphasising both past causes of failure and main drivers of success. Invited Speaker As SDL Language Weaver's CTO, Daniel leads the R&D efforts focused on automated translation technology. Prior to cofounding Language Weaver, Daniel was a Research Associate Professor in Computer Science at the University of Southern California, a position he still holds. He is recognised as a leading authority in natural language processing, machine translation, discourse parsing and text summarisation. Daniel has more than 20 patents awarded or pending and has published an MIT press book and more than 100 peer reviewed articles. Daniel received his Ph.D. in Computer Science from the University of Toronto.
Proceedings of the First Workshop on Human-Informed Translation and Interpreting Technology
Proceedings of the First Workshop on Human-Informed Translation and Interpreting Technology, 2017
This paper describes an approach to translating course unit descriptions from Italian and German into English, using a phrase-based machine translation (MT) system. The genre is very prominent among those requiring translation by universities in European countries in which English is a non-native language. For each language combination, an in-domain bilingual corpus including course unit and degree program descriptions is used to train an MT engine, whose output is then compared to a baseline engine trained on the Europarl corpus. In a subsequent experiment, a bilingual terminology database is added to the training sets in both engines and its impact on the output quality is evaluated based on BLEU and postediting score. Results suggest that the use of domain-specific corpora boosts the engines quality for both language combinations, especially for German-English, whereas adding terminological resources does not seem to bring notable benefits.