Comparing translator acceptability of TM and SMT outputs

This paper reports on an initial study that aims to understand whether the acceptability of translation memory (TM) among translators when contrasted with machine translation (MT) unacceptability is based on users’ ability to optimise precision in match suggestions. Seven translators were asked to rate whether 60 English-German translated segments were a usable basis for a good target translation. 30 segments were from a domain-appropriate TM without a quality threshold being set, and 30 segments were translated by a general domain statistical MT system. Participants found the MT output more useful on average, with only TM fuzzy matches of over 90% considered more useful. This result suggests that, were the MT community able to provide an accurate quality threshold to users, they would consider MT to be the more useful technology

On the complementarity between human translators and machine translation

Many translators are fearful of the impact of Machine Translation (MT) on their profession, broadly speaking, and on their livelihoods more specifically. We contend that their concern is misplaced, as human translators have a range of skills, many of which are currently – with no signs of any imminent breakthroughs on the horizon – impossible to replicate by automatic means. Nonetheless, in this paper, we will show that MT engines have considerable potential to improve translators’ productivity and ensure that the output translations are more consistent. Furthermore, we will investigate what machines are good at, where they break down, and why the human is likely to remain the most critical component in the translation pipeline for many years to com

On the Complementarity between Human Translators and Machine Translation

Many translators are fearful of the impact of Machine Translation (MT) on their profession, broadly speaking, and on their livelihoods more specifically. We contend that their concern is misplaced, as human translators have a range of skills, many of which are currently – with no signs of any imminent breakthroughs on the horizon – impossible to replicate by automatic means. Nonetheless, in this paper, we will show that MT engines have considerable potential to improve translators’ productivity and ensure that the output translations are more consistent. Furthermore, we will investigate what machines are good at, where they break down, and why the human is likely to remain the most critical component in the translation pipeline for many years to come

Mereix la pena mostrar sempre la traducció automàtica?

En aquest treball de fi de grau estudiarem experimentalment i en un entorn controlat com treballa més ràpidament un traductor quan postedita la traducció automàtica: quan se li mostren les traduccions automàtiques de tots els segments en un sistema de traducció assistida o establint un llindar en la qualitat estimada de la traducció que determine si se li ha de mostrar o no. En primera aproximació, per a calcular l'estimació de la qualitat s'usaran traduccions de referència posteditades per als segments del treball, per a posteriorment calcular la taxa d’errors per paraula. Els experiments es realitzaran amb estudiants de l’últim curs de traducció i provaran d'establir el llindar òptim si n'hi ha. L'estudi està motivat pel treball de Joss Moorkens i Andy Way «Comparing Translator Acceptability of TM and SMT Outputs», publicat en les actes del congrés EAMT 2016

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Post-editing neural machine translation versus translation memory segments

The use of neural machine translation (NMT) in a professional scenario implies a number of challenges despite growing evidence that, in language combinations such as English to Spanish, NMT output quality has already outperformed statistical machine translation in terms of automatic metric scores. This article presents the result of an empirical test that aims to shed light on the differences between NMT postediting and translation with the aid of a translation memory (TM). The results show that NMT postediting involves less editing than TM segments, but this editing appears to take more time, with the consequence that NMT post-editing does not seem to improve productivity as may have been expected. This might be due to the fact that NMT segments show a higher variability in terms of quality and time invested in post-editing than TM segments that are 'more similar' on average. Finally, results show that translators who perceive that NMT boosts their productivity actually performed faster than those who perceive that NMT slows them dow

Productivity and Lexical Pragmatic Features in a Contemporary CAT Environment: An Exploratory Study in English to Japanese

As the translation profession has become more technologized, translators increasingly work within an interface that combines translation from scratch, translation memory suggestions, machine translation post-editing, and terminological resources. This study analyses user activity data from one such interface, and measures temporal effort for English to Japanese translation at the segment level. Using previous studies of translation within the framework of relevance theory as a starting point, various features and edits were identified and annotated within the texts, in order to find whether there was a relationship between their prevalence and translation effort. Although this study is exploratory in nature, there was an expectation based on previous studies that procedurally encoded utterances would be associated with greater translation effort. This expectation was complicated by the choice of a language pair in which there has been little research applying relevance theory to translation, and by contemporary research that has made the distinction between procedural and conceptual encoding appear more fluid than previously believed. Our findings are that some features that lean more towards procedural encoding (such as prevalence of pronouns and manual addition of postpositions) are associated with increased temporal effort, although the small sample size makes it impossible to generalise. Segments translated with the aid of translation memory showed the least average temporal effort, and segments translated using machine translation appeared to require more effort than translation from scratch

Apps-based Machine Translation on Smart Media Devices - A Review

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists

Quality expectations of machine translation

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself