A Translator's View about Translation Memory and Machine Translation Integration

2013 ◽  
Vol 0 (23) ◽  
Author(s):  
Dainora Maumevičienė ◽  
Aušra Berkmanienė
2021 ◽  
Author(s):  
Deng Cai ◽  
Yan Wang ◽  
Huayang Li ◽  
Wai Lam ◽  
Lemao Liu

Author(s):  
Kristine Bundgaard

Today technology is part and parcel of professional translation, and translation has therefore been characterised as Translator-Computer Interaction (TCI) (O’Brien 2012). Translation is increasingly carried out using Translation Memory (TM) systems which incorporate machine translation (MT), referred to as MT-assisted TM translation, and in this type of tool, translators switch between editing TM matches and post-editing MT matches. It is generally assumed that translators’ attitudes towards technology impact on this interaction with the technology. Drawing on Eagly/Chaiken’s (1995) definition of attitudes as evaluations of entities with favour or disfavour and on qualitative data from a workplace study of TCI, conducted as part of a PhD dissertation (Bundgaard 2017) and partly reported on in Bundgaard et al. (2016), this paper explores translator attitudes towards TCI in the form of MT-assisted TM translation. In doing so, the paper has a particular focus on the disfavour towards TCI expressed by translators. Moreover, inspired by Olohan (2011), who applies Pickering’s “mangle of practice” theory and analyses resistance and accommodation in TCI, the paper focuses on how translators accommodate resistances offered by the tool. The study shows that the translators express disfavour towards MT in many respects, but also acknowledge positive aspects of the technology and expect MT to play a significant role in their future working lives. The translators do not make many positive or negative comments about TM which might indicate that TM is a completely integrated part of their processes. The translators seem to have a flexible and pragmatic attitude towards TCI, adapting to the tool’s imperfections and accommodating its resistances.


Author(s):  
Joss Moorkens ◽  
Ryoko Sasamoto

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.


2014 ◽  
Vol 687-691 ◽  
pp. 1708-1711 ◽  
Author(s):  
Xin Xin Chen

This paper mainly studied how to inject the project team management into the collaborative translation tools based on the cloud platform, so as to better coordinate the relationship between different translators and translation project, and the aided translation input method will be introduced to collaborative translation platform, as a bridge between the translator and the collaborative translation platform, to effectively improve the efficiency of different translators. At the same time we realize the terms detection and recognition system and translation memory system, to not only improve the efficiency of different translators, but also can solve the problem that translation content repetition and lack of communication between different translators.


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