MAHT systems do not attempt to produce target texts without the intervention of the human translator but to help him/her in achieving highe r productivity rates while increasing the final quality. The first goal is commonly achieved by building a translation memory, a (usually large) base of source-totarget text fragments that the system tries to employ when newly (though similar) source text i...

The article is trying to highlight the major skills of a presentday translator, without which failure is assured. Although we start with general (classical) requirements, particular ones will be discussed, such as the gradual shift from PRAT (paper-and-rubber-assisted translation) to CAT (computer-assisted translation). We argue that professional translators in the 21st century must make use of...

The translation of natural languages by machine, first dreamt of in the seventeenth century, has become a reality in the late twentieth. Computer programs are producing translations-not perfect translations, for that is an ideal to which no human translator can aspire; nor translations of literary texts, for the subtleties and nuances of poetry are beyond computational analysis; but translation...

At present, the task of post-editing Machine Translation (MT) is commonly carried out via Translation Memory (TM) tools that, while well-suited for editing of TM matches, do not fully support MT post-editing. This paper describes the results of a survey of professional translators and post-editors, in which they chose features and functions that they would like to see in translation and post-ed...

Avery hot issue for research and industry is how to effectively integratemachine translation (MT)within computer assisted translation (CAT) software. This paper focuses on this issue, and more generally how to dynamically adapt phrase-based statistical machine translation (SMT) by exploiting external knowledge, like the post-editions from professional translators. We present an enhancement of t...

Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows. Usually, human judgments come in the form of ranking outputs of different translation systems and recently, post-edits of MT output have come into focus. This paper describes the results of a detailed lar...

Professional human translators usually do not employ the concept of word alignments, producing translations ‘sense-forsense’ instead of ‘word-for-word’. This suggests that unalignable words may be prevalent in the parallel text used for machine translation (MT). We analyze this phenomenon in-depth for Chinese-English translation. We further propose a simple and effective method to improve autom...

This qualitative research aims to investigate and analyze the practice of post-editing activity in Saudi translation agencies centers. The study’s focus is on importance Machine Translation (MT) output as an approach upgrade quality MT results enhance accuracy translations. study data were collected through electronic survey (questionnaire) comprising short questions about guidelines addition t...

Active learning is a framework that makes it possible to efficiently train statistical models by selecting informative examples from a pool of unlabeled data. Previous work has found this framework effective for machine translation (MT), making it possible to train better translation models with less effort, particularly when annotators translate short phrases instead of full sentences. However...

This paper reports upon a survey carried out among thirty-eight trainee translators who took courses on machine translation. The survey was conducted asking the sample of students to fill out a questionnaire both at the beginning and at the end of the MT course. The questions aimed at assessing the degree of knowledge about MT of the respondents and the opinions and impressions that they accord...