1. Introduction Ever since the idea of using computers to translate natural languages was first proposed in the 1940s and since the first investigations were begun in the 1950s, translators have watched developments either in scorn or in trepidation. Either they have dismissed the very notion that anyone could even believe that translation could be mechanized, or (at the other extreme) they hav...

This paper studies the minimum score at which machine translation (MT) evaluation metrics report productivity gains in a machine translation post-editing (MTPE) task. We ran an experiment involving 10 professional in-house translators from our company in which they were asked to carry out a real translation task involving MTPE, translation from scratch and fuzzymatch editing. We then analyzed t...

We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to English MT, we developed and trained a transliterator on a bitext of 7 million sentences and Google’s English terabyte ngrams and achieved better name translation accuracy than 3 out of 4 professional translators. The pape...

Machine Translation (MT) has experienced remarkable improvements and consequently grown in popularity of late. It now functions not only as an end in itself but also as a valuable asset to be exploited by translators in the promising practice of post-editing the outcome of MT systems, which can yield faster and sometimes more accurate results. Most systems, however, were not originally designed...

aids for translators are beginning to look inviting and actual machine translation (MT), thrown ignominiously out of fashion by the ALPAC report in 1966, is now in use. Only our scepticism is unchanged. We remain wary, especially of MT. Yet although machine translat ion – or rather, in most cases, machine pretranslation – certainly has farreaching implications, it is not, it seems, a threat to ...

We carried out a study on monolingual translators with no knowledge of the source language, but aided by post-editing and the display of translation options. On Arabic-English and Chinese-English, using standard test data and current statistical machine translation systems, 10 monolingual translators were able to translate 35% of Arabic and 28% of Chinese sentences correctly on average, with so...

In this paper we share our experience from implementing machine translation in localization into relatively small languages of the three Baltic countries – Latvian, Lithuanian, and Estonian. We describe our approach in improving terminology translation and consistency by preprocessing of the source text and performing term integration. We present results of a formal evaluation of MT impact on t...

The present study aims to examine the adverse effects of absence Computer-assisted Translation (CAT) tool course syllabi in Libyan universities on translation quality government institutions. This research is motivated by underutilization CAT tools departments and lack education their use universities, which may result a shortage professional translators proficient utilizing these tools. employ...

This article investigates perceptions of technology-mediated translations literary texts by two groups: translation students and professional translators. The participants post-edited an excerpt from a classic Dickens novel into Turkish using machine (MT) system their choice. analysis the texts, participants’ answers to survey questions, interviews with translators suggest that MT is currently ...

The recent technological shift in machine translation from statistical machine translation (SMT) to neural machine translation (NMT) raises the question of the strengths and weaknesses of NMT. In this paper, we present an analysis of NMT and SMT systems’ outputs from narrow domain English-Latvian MT systems that were trained on a rather small amount of data. We analyze post-edits produced by pr...