- Guildford, United Kingdom
- Kocaeli University, Foreign Languages Department, Department Memberadd
The paper reports on a pilot study conducted to test the methodology to replicate the study by Jensen & Pavlović (2009) which investigates the effect of translation directionality on cognitive processing by means of eye-tracking. The... more
The paper reports on a pilot study conducted to test the methodology to replicate the study by Jensen & Pavlović (2009) which investigates the effect of translation directionality on cognitive processing by means of eye-tracking. The following hypotheses are retested: (1) In both directions of translation, processing the TT requires more cognitive effort than processing the ST; (2) L2 translation tasks on the whole require more cognitive effort than L1 tasks; (3) cognitive effort invested in the processing of the ST is higher in L1 translation than in L2 translation; (4) cognitive effort invested in the processing of the TT is higher in L2 translation than in L1 translation. The results showed that the findings of three out of four hypotheses were the same as the findings of Jensen & Pavlović (2009). Both studies suggest that neither processing the texts in L2 (ST or TT) nor translation into L2 leads to a higher amount of cognitive effort. The findings are important in that they challenge the traditional view of directionality that is based on traditional assumptions rather than empirical data. This pilot study is distinctive in that it is the first study in Turkey that uses eye-tracking to explore the translation process (Temizöz 2009).
Keywords: Directionality, L2 translation, L1 translation, cognitive processing in the translation process, eye-tracking.
Keywords: Directionality, L2 translation, L1 translation, cognitive processing in the translation process, eye-tracking.
Research Interests:
In parallel with the rise of MT and the integration of machine translated segments into the translation workflow as TM input, empirical research on MT has gained momentum from the turn of the new millennium. This report covers empirical... more
In parallel with the rise of MT and the integration of machine translated segments into the translation workflow as TM input, empirical research on MT has gained momentum from the turn of the new millennium. This report covers empirical studies on machine translation and the postediting of MT output. It includes a synoptic table giving the author and year of each experiment, the number of participants, brief information on the participant profile, type of text used, the number of words in the texts, language pair and direction used, and the name of the machine translation and/or translation memory system used.
Research Interests:
This study compares the quality of postediting performed by subject-matter experts as opposed to professional translators. A total of 10 professional translators and 10 engineers postedited a 482-word technical text pre-translated from... more
This study compares the quality of postediting performed by
subject-matter experts as opposed to professional translators. A total of 10 professional translators and 10 engineers postedited a 482-word technical text pre-translated from English into Turkish using data-based machine translation system, Google Translate. The findings suggest that, for this particular task (technical translation), translators’ and engineers’ postediting quality is similar as far as the categories of mistranslation, accuracy, and consistency are concerned. Engineers performed significantly better than translators only in the terminology category. In the language category, translators made significantly fewer (minor) errors than engineers. The qualitative data analysis revealed that, for this particular task, a degree in translation does not directly correlate with postediting quality, unless it is combined with
subject-matter knowledge and professional experience in
translation. Finally, the present study indicates that – both for the engineers and the professional translators – expertise and
experience in the subject matter are important factors determining postediting quality.
subject-matter experts as opposed to professional translators. A total of 10 professional translators and 10 engineers postedited a 482-word technical text pre-translated from English into Turkish using data-based machine translation system, Google Translate. The findings suggest that, for this particular task (technical translation), translators’ and engineers’ postediting quality is similar as far as the categories of mistranslation, accuracy, and consistency are concerned. Engineers performed significantly better than translators only in the terminology category. In the language category, translators made significantly fewer (minor) errors than engineers. The qualitative data analysis revealed that, for this particular task, a degree in translation does not directly correlate with postediting quality, unless it is combined with
subject-matter knowledge and professional experience in
translation. Finally, the present study indicates that – both for the engineers and the professional translators – expertise and
experience in the subject matter are important factors determining postediting quality.
Research Interests:
Counting and not counting recurring errors are two different methods that have been employed in translation quality evaluation without paying due attention to how the difference between the results of each method, if any, affects the... more
Counting and not counting recurring errors are two different methods that have been employed in translation quality evaluation without paying due attention to how the difference between the results of each method, if any, affects the quality score of the end product, thereby affecting the validity of the quality evaluation method in question. This paper reports on a study which shows that penalizing or not penalizing recurring errors in the target text significantly affects the quality score. The results reveal a need for a more critical approach in handling recurring errors in translation quality evaluation.
Research Interests:
Collaborative practice, as defined by Vangrieken et al. (2015) in educational context, occurs when individuals work within a group, communicating and contributing during activities while working towards a shared goal or specific task... more
Collaborative practice, as defined by Vangrieken et al. (2015) in educational context, occurs when individuals work within a group, communicating and contributing during activities while working towards a shared goal or specific task outcome. In order to successfully execute a collaborative task in an online environment, a technological solution based on a robust design is required. An example of such design is the 3C Collaboration Model which consists of three components: Communication (exchange of messages and information amongst people), Coordination (management of people, their activities and resources), and Cooperation (the production taking place in a shared workspace) (Fuks et al. 2008).
Well-designed collaborative tools are needed to support growth in collaborative and team-based work, which has recently seen a sharp rise across industries. According to Hollenbeck et al. (2012), team-based work increased from 20% in 1980 to 80% in 2010. Gartner’s (2021) Digital Worker Experience Survey reports a 44% increase in the use of collaboration tools since 2019, with nearly 80% of workers using collaboration tools in 2021.
The language industry has also seen a change in working practices due to the rapid development of new technologies for collaborative translation (Cordingley and Frigau Manning 2017, Fırat 2021, García 2015, García 2017, Gough and Perdikaki 2018, Moorkens 2020, O’Hagan 2013, Spinuzzi 2007, Tsai 2020, Zwischenberger 2021). This includes collaborative translation via networked CAT tools, translation management systems, crowdsourcing platforms and other technologies in which multiple agents can work on the same project by means of horizontal collaboration (between translators) and vertical collaboration (between translators, editors, project managers, clients etc.) (CSA 2021).
For translators, the fast increase in the range of technologies they are required to use means a sharp learning curve. Common Sense Advisory (CSA) report that, out of 562 surveyed freelance translators, 43% commonly use collaborative translation platforms in their work (CSA 2021: 10), with an average of five (CSA 2020: 39). Although translators have generally embraced the change, with such a fundamental shift in the working methodologies, often imposed top down on translators, they are right to demand the tools to be fit for purpose, user-friendly, efficient, and enjoyable to use (LeBlanc 2017).
Consequently, translators should have the right to demand that the ‘collaborative’ tools they use are truly collaborative. However, the term ‘collaborative’ seems to be generally accepted to refer only to describe the new workflows and the tools that enable horizontal and vertical collaboration. Little is known about how well these tools actually support collaboration in terms of communication, coordination and cooperation, the fundamental building blocks of a collaborative environment.
This paper reports on a survey-based study investigating a form of collaborative translation, concurrent translation (CT), where translators and other agents work on the same text simultaneously using horizontal and vertical collaboration. More specifically, this paper reports on how CT is perceived by 804 translators working with 51 platforms, and their suggestions on how the platforms can be improved. Concurrent Translation is distinguished as a form of collaborative translation because collaborative platforms are also used for non-simultaneous collaborations and for one translator jobs using only vertical collaboration.
The data gathered via the questionnaire is interpreted against the 3C Collaboration Model (Fuks et al, 2008). The results reveal that translators are not always enthusiastic about the design of the shared workspace for CT on the platforms in all three components of the model. In terms of Communication, the collaborative tools which enable CT appear to be insufficiently supported by the built-in communication tools, which calls for better design and management of communication during CT. With regard to Coordination, the management of people and their activities (e.g work allocation) as well as resources (e.g translation memory, machine translation and terminology resources) needs improvement in order to support CT. Privacy issues, technical issues and lack of training/briefing on how to work on the platforms were also raised as part of this component. Lastly, when it comes to Cooperation, the respondents felt that when working in CT workflow they often lacked a shared goal and that CT can engender behaviour more akin to competition than to cooperation, thus resulting in the lower quality of the produced output and higher levels of mental stress.
The findings suggest that, from the perspective of translators, the collaborative translation tools currently on the market need improvement. The concrete improvements suggested by the survey respondents in all three areas of the 3C model could potentially make the CT workflow more effective by improving user experience, leading to more collaborative experiences, and, eventually, to higher quality outputs.
References:
Cordingley, A., and Frigau Manning, C. (2017). “What is Collaborative Translation?” In Collaborative Translation: From the Renaissance to the Digital Age, ed. by Anthony Cordingley and Cèline Frigau Manning, 1-30. London: Bloomsbury
CSA Research (2020). “The State of the Linguist Supply Chain: Translators and Interpreters 2020” (January 2020), Hélène Pielmeier and Paul O’Mara.
CSA Research (2021). “Collaborative Translation Platforms: The Reality of Sharing Language Projects” (May 2021), Alison Toon and Hélène Pielmeier.
Fırat, G. (2021). "Uberization of Translation: Impacts on Working Conditions". In The Journal of Internationalization and Localization (JIAL) 8 (1) by Julie McDonough Dolmaya and Minako O'Hagan (Eds). John Benjamins.
Fuks, H., Raposo, A., Gerosa, M.A., Pimental, M. and Lucena, C.J.P. (2008) The 3C Collaboration Model. In: Ned Kock, Encyclopeadia of E-Collaboration, Information Science Reference, IGI Global: Hershey and New York
García, I. (2015). “Cloud Marketplaces: Procurement of Translators in the Age of Social Media” Journal of Specialised Translation 23, 18-38.
García, I. (2017) “Translating in the Cloud Age: Online Marketplaces”. HERMES - Journal of Language and Communication in Business, (56): 59.
Gartner (2021). Online. https://www.gartner.com/en/newsroom/press-releases/2021-08-23-gartner-survey-reveals-44-percent-rise-in-workers-use-of-collaboration-tools-since-2019)
Gough, J. & Perdikaki, K. (2018). “Concurrent Translation - Reality or Hype?”. In Translating and The Computer 40, AsLing Proceedings, 79-88. London. 15-16 November 2018.
Hollenbeck, J. R., Beersma, B. & Schouten, M. E. (2012). Beyond Team Types and Taxonomies: A Dimensional Scaling Conceptualization for Team Description. The Academy of Management Review, 37(1), 82–106. http://www.jstor.org/stable/23218853
LeBlanc, M. (2017) “'I Can't Get No Satisfaction!' Should We Blame Translation Technologies or Shifting Business Practices?”. In Kenny, D. (Eds.), Human Issues in Translation Technology, London/New York: Routledge, pp.45-62.
Moorkens, J. (2020). “A Tiny Cog in a Large Machine: Digital Taylorism in the Translation Industry”. In Translation Spaces: Fair MT - Towards Ethical, Sustainable Machine Translation, 9 (1): 12-34. John Benjamins Publishing Company.
O’Hagan, M. (2013). The impact of new technologies on translation studies: A technological turn? In C. Millán & F. Bartrina (Eds.), The Routledge handbook of translation studies (pp. 503–518). London, UK: Routledge.
Spinuzzi, C. (2007). Technical Communication in the Age of Distributed Work. Technical Communication Quarterly, 16, 265-277. https://doi.org/10.1080/10572250701290998
Tsai, Y. (2020). "Collaborative Translation in the Digital Age," Research in Language: Vol. 18: Iss. 2, Article 2. Available at: https://digijournals.uni.lodz.pl/rela/vol18/iss2/2
Zwischenberger, C. (2021). Online collaborative translation: its ethical, social, and conceptual conditions and consequences, Perspectives, DOI: 10.1080/0907676X.2021.1872662
Vangrieken, K., Dochy, F., Raes, E. & Kyndt, E. (2015). Teacher collaboration: A systematic review, Educational Research Review, Vol. 15, pages 17-40
Well-designed collaborative tools are needed to support growth in collaborative and team-based work, which has recently seen a sharp rise across industries. According to Hollenbeck et al. (2012), team-based work increased from 20% in 1980 to 80% in 2010. Gartner’s (2021) Digital Worker Experience Survey reports a 44% increase in the use of collaboration tools since 2019, with nearly 80% of workers using collaboration tools in 2021.
The language industry has also seen a change in working practices due to the rapid development of new technologies for collaborative translation (Cordingley and Frigau Manning 2017, Fırat 2021, García 2015, García 2017, Gough and Perdikaki 2018, Moorkens 2020, O’Hagan 2013, Spinuzzi 2007, Tsai 2020, Zwischenberger 2021). This includes collaborative translation via networked CAT tools, translation management systems, crowdsourcing platforms and other technologies in which multiple agents can work on the same project by means of horizontal collaboration (between translators) and vertical collaboration (between translators, editors, project managers, clients etc.) (CSA 2021).
For translators, the fast increase in the range of technologies they are required to use means a sharp learning curve. Common Sense Advisory (CSA) report that, out of 562 surveyed freelance translators, 43% commonly use collaborative translation platforms in their work (CSA 2021: 10), with an average of five (CSA 2020: 39). Although translators have generally embraced the change, with such a fundamental shift in the working methodologies, often imposed top down on translators, they are right to demand the tools to be fit for purpose, user-friendly, efficient, and enjoyable to use (LeBlanc 2017).
Consequently, translators should have the right to demand that the ‘collaborative’ tools they use are truly collaborative. However, the term ‘collaborative’ seems to be generally accepted to refer only to describe the new workflows and the tools that enable horizontal and vertical collaboration. Little is known about how well these tools actually support collaboration in terms of communication, coordination and cooperation, the fundamental building blocks of a collaborative environment.
This paper reports on a survey-based study investigating a form of collaborative translation, concurrent translation (CT), where translators and other agents work on the same text simultaneously using horizontal and vertical collaboration. More specifically, this paper reports on how CT is perceived by 804 translators working with 51 platforms, and their suggestions on how the platforms can be improved. Concurrent Translation is distinguished as a form of collaborative translation because collaborative platforms are also used for non-simultaneous collaborations and for one translator jobs using only vertical collaboration.
The data gathered via the questionnaire is interpreted against the 3C Collaboration Model (Fuks et al, 2008). The results reveal that translators are not always enthusiastic about the design of the shared workspace for CT on the platforms in all three components of the model. In terms of Communication, the collaborative tools which enable CT appear to be insufficiently supported by the built-in communication tools, which calls for better design and management of communication during CT. With regard to Coordination, the management of people and their activities (e.g work allocation) as well as resources (e.g translation memory, machine translation and terminology resources) needs improvement in order to support CT. Privacy issues, technical issues and lack of training/briefing on how to work on the platforms were also raised as part of this component. Lastly, when it comes to Cooperation, the respondents felt that when working in CT workflow they often lacked a shared goal and that CT can engender behaviour more akin to competition than to cooperation, thus resulting in the lower quality of the produced output and higher levels of mental stress.
The findings suggest that, from the perspective of translators, the collaborative translation tools currently on the market need improvement. The concrete improvements suggested by the survey respondents in all three areas of the 3C model could potentially make the CT workflow more effective by improving user experience, leading to more collaborative experiences, and, eventually, to higher quality outputs.
References:
Cordingley, A., and Frigau Manning, C. (2017). “What is Collaborative Translation?” In Collaborative Translation: From the Renaissance to the Digital Age, ed. by Anthony Cordingley and Cèline Frigau Manning, 1-30. London: Bloomsbury
CSA Research (2020). “The State of the Linguist Supply Chain: Translators and Interpreters 2020” (January 2020), Hélène Pielmeier and Paul O’Mara.
CSA Research (2021). “Collaborative Translation Platforms: The Reality of Sharing Language Projects” (May 2021), Alison Toon and Hélène Pielmeier.
Fırat, G. (2021). "Uberization of Translation: Impacts on Working Conditions". In The Journal of Internationalization and Localization (JIAL) 8 (1) by Julie McDonough Dolmaya and Minako O'Hagan (Eds). John Benjamins.
Fuks, H., Raposo, A., Gerosa, M.A., Pimental, M. and Lucena, C.J.P. (2008) The 3C Collaboration Model. In: Ned Kock, Encyclopeadia of E-Collaboration, Information Science Reference, IGI Global: Hershey and New York
García, I. (2015). “Cloud Marketplaces: Procurement of Translators in the Age of Social Media” Journal of Specialised Translation 23, 18-38.
García, I. (2017) “Translating in the Cloud Age: Online Marketplaces”. HERMES - Journal of Language and Communication in Business, (56): 59.
Gartner (2021). Online. https://www.gartner.com/en/newsroom/press-releases/2021-08-23-gartner-survey-reveals-44-percent-rise-in-workers-use-of-collaboration-tools-since-2019)
Gough, J. & Perdikaki, K. (2018). “Concurrent Translation - Reality or Hype?”. In Translating and The Computer 40, AsLing Proceedings, 79-88. London. 15-16 November 2018.
Hollenbeck, J. R., Beersma, B. & Schouten, M. E. (2012). Beyond Team Types and Taxonomies: A Dimensional Scaling Conceptualization for Team Description. The Academy of Management Review, 37(1), 82–106. http://www.jstor.org/stable/23218853
LeBlanc, M. (2017) “'I Can't Get No Satisfaction!' Should We Blame Translation Technologies or Shifting Business Practices?”. In Kenny, D. (Eds.), Human Issues in Translation Technology, London/New York: Routledge, pp.45-62.
Moorkens, J. (2020). “A Tiny Cog in a Large Machine: Digital Taylorism in the Translation Industry”. In Translation Spaces: Fair MT - Towards Ethical, Sustainable Machine Translation, 9 (1): 12-34. John Benjamins Publishing Company.
O’Hagan, M. (2013). The impact of new technologies on translation studies: A technological turn? In C. Millán & F. Bartrina (Eds.), The Routledge handbook of translation studies (pp. 503–518). London, UK: Routledge.
Spinuzzi, C. (2007). Technical Communication in the Age of Distributed Work. Technical Communication Quarterly, 16, 265-277. https://doi.org/10.1080/10572250701290998
Tsai, Y. (2020). "Collaborative Translation in the Digital Age," Research in Language: Vol. 18: Iss. 2, Article 2. Available at: https://digijournals.uni.lodz.pl/rela/vol18/iss2/2
Zwischenberger, C. (2021). Online collaborative translation: its ethical, social, and conceptual conditions and consequences, Perspectives, DOI: 10.1080/0907676X.2021.1872662
Vangrieken, K., Dochy, F., Raes, E. & Kyndt, E. (2015). Teacher collaboration: A systematic review, Educational Research Review, Vol. 15, pages 17-40
Research Interests:
The advent of cloud-based collaborative translation platforms augmented with machine translation (MT) and AI, has brought about the biggest change in the translation process and workflows since the introduction of CAT tools, and has... more
The advent of cloud-based collaborative translation platforms augmented with machine translation (MT) and AI, has brought about the biggest change in the translation process and workflows since the introduction of CAT tools, and has enabled new forms of collaboration in translation. Cloud-based translation platforms allow simultaneous, non-linear and fast collaboration (CSA Research 2021) between multiple agents including translators, editors, subject-matter experts, and clients, and are used to perform paid translation tasks. However, non-simultaneous workflows on collaborative platforms still seem to be the dominant model. To distinguish the specific, simultaneous and collaborative nature of translation from other, non-simultaneous or unpaid forms, we refer to it as Concurrent Translation (CT). In this mode, a text is simultaneously/concurrently translated by a number of translators using a common translation memory. A text can be split, and segments assigned to individual translators or translators can select segments on a first-come-first-served basis.
The selling points usually associated with the strengths of the new technologies and workflows, such as reduced cost, increased speed, ensured quality and facilitated communication, have created an increased demand for this type of service and the number of collaborative platforms which enable CT workflows rose exponentially in the recent years (Nimdzi online). However, as Gough and Perdikaki (2018: 79) point out, “very little is known about […] how this new mode of collaborative translation affects the product and the process of translation”. Although the collaborative nature of translation (both between translators and between translators and other agents involved in the process) is not a new phenomenon (Alfer 2017, Cordingley and Frigau Manning 2016, O’Brien 2011, Trzeciak-Huss 2018) and brings numerous benefits, certain aspects of the CT workflow can be seen as problematic, especially from the translator point of view. This requires an investigation in order to make sure that all stakeholders are aware of the potential benefits and challenges associated with this workflow and to encourage the refinement of the workflow to optimise its potential.
The recent CSA Research (2021) survey on collaborative platforms provide insights into translation on platforms; however, not all collaborative translation is in concurrent mode, and the present survey specifically focuses on concurrent translation paradigm within the collaborative translation. Following initial investigations via pilot studies (Gough and Perdikaki 2018), the present paper reports on the findings from an online survey carried out amongst 804 translators working on collaborative platforms in concurrent mode to answer the research questions regarding platform types, time spent, text types translated, as well as the profile and experiences of translators working in CT mode.
Translators surveyed in this study represented workers on 55 translation platforms, including platforms with a) ‘split and assign’ approach where a project manager splits a text and assigns it to a limited number of translators and b) ‘1st come 1st served’ approach – a more automated approach where a text is made available to an unlimited number of translators who pick segments on the first come first served basis. Data suggests that CT is by no means a marginal trend within the industry. Although CT does not seem to be a mainstream workflow, it cannot go unnoticed as 86% of the participants spend up to 40% of their working time working in this mode.
Data reveals that almost half of the participants (48%) do not prefer CT workflow to traditional one-translator jobs (35% remaining neutral, 17% prefer CT). A majority of the respondents (63%) think that CT workflow on translation platforms increases the sense of unhealthy competition between the translators working on the same project. This is even stronger for the users of platforms belonging to 1st come 1st served approach with 75% of the respondents saying that CT mode increases destructive competition. Quality seems to be the first and foremost aspect that is affected adversely by the issues identified in CT workflow. More than half of the participants (59%) who provided free comments think that quality is negatively affected by the CT workflow.
Overall, findings suggest that CT has implications on the translation process, product and the translator. Despite the affordances such as teamwork, peer learning, cooperative competition, the flexibility of volume of work and working time; CT workflow comes with its challenges such as excessive time pressure, aggressive competition, reduced research and self-revision in the translation process, quality compromised for speed, lack of or limited control over the workflow and the final quality of the translation or lack of trust among collaborators. Translators often felt unprepared for the new workflow in terms of briefing or training or simply did not know enough about the functionalities within the platforms. Coupled with the lack of sufficient remuneration, these challenges might contribute to lack of ownership of the translation task as a whole, lack of satisfaction and devaluation of translation in the eyes of the respondents.
In this presentation, we will focus on the selected findings from the survey to highlight the perceived benefits and the most critical challenges that we have identified in the data.
References:
Alfer, Alexa. 2017. “Entering the Translab” In Translation and Translanguaging in Multilingual Contexts - Volume 3, Issue 3, pp. 275-290
Cordingley, Anthony, and Cèline Frigau Manning. 2017. “What is Collaborative Translation?” In Collaborative Translation: From the Renaissance to the Digital Age, ed. by Anthony Cordingley and Cèline Frigau Manning, 1-30. London: Bloomsbury.
CSA Research. 2021. “Collaborative Translation Platforms: The Reality of Sharing Language Projects” (May 2021), Alison Toon and Hélène Pielmeier.
Gough, Joanna, & Katerina Perdikaki. 2018. “Concurrent Translation - Reality or Hype?”. In Translating and The Computer 40, AsLing Proceedings, 79-88. London. 15-16 November 2018.
O’Brien, Sharon. 2011. “Collaborative Translation.” In Handbook of Translation Studies, ed. by Yves Gambier and Luc van Doorslaer, 17- 20. Amsterdam: John Benjamins.
Nimdzi Language Technology Atlas. (Accessed in January 2022) https://www.nimdzi.com/advanced-search/?x=12&y=11&_adv_post_search=atlas
Trzeciak-Huss, Joanna. 2018. “Collaborative Translation”. In Washbourne, K., & Wyke, B.V. (Eds.). The Routledge Handbook of Literary Translation (1st ed.). Routledge. https://doi.org/10.4324/9781315517131
The selling points usually associated with the strengths of the new technologies and workflows, such as reduced cost, increased speed, ensured quality and facilitated communication, have created an increased demand for this type of service and the number of collaborative platforms which enable CT workflows rose exponentially in the recent years (Nimdzi online). However, as Gough and Perdikaki (2018: 79) point out, “very little is known about […] how this new mode of collaborative translation affects the product and the process of translation”. Although the collaborative nature of translation (both between translators and between translators and other agents involved in the process) is not a new phenomenon (Alfer 2017, Cordingley and Frigau Manning 2016, O’Brien 2011, Trzeciak-Huss 2018) and brings numerous benefits, certain aspects of the CT workflow can be seen as problematic, especially from the translator point of view. This requires an investigation in order to make sure that all stakeholders are aware of the potential benefits and challenges associated with this workflow and to encourage the refinement of the workflow to optimise its potential.
The recent CSA Research (2021) survey on collaborative platforms provide insights into translation on platforms; however, not all collaborative translation is in concurrent mode, and the present survey specifically focuses on concurrent translation paradigm within the collaborative translation. Following initial investigations via pilot studies (Gough and Perdikaki 2018), the present paper reports on the findings from an online survey carried out amongst 804 translators working on collaborative platforms in concurrent mode to answer the research questions regarding platform types, time spent, text types translated, as well as the profile and experiences of translators working in CT mode.
Translators surveyed in this study represented workers on 55 translation platforms, including platforms with a) ‘split and assign’ approach where a project manager splits a text and assigns it to a limited number of translators and b) ‘1st come 1st served’ approach – a more automated approach where a text is made available to an unlimited number of translators who pick segments on the first come first served basis. Data suggests that CT is by no means a marginal trend within the industry. Although CT does not seem to be a mainstream workflow, it cannot go unnoticed as 86% of the participants spend up to 40% of their working time working in this mode.
Data reveals that almost half of the participants (48%) do not prefer CT workflow to traditional one-translator jobs (35% remaining neutral, 17% prefer CT). A majority of the respondents (63%) think that CT workflow on translation platforms increases the sense of unhealthy competition between the translators working on the same project. This is even stronger for the users of platforms belonging to 1st come 1st served approach with 75% of the respondents saying that CT mode increases destructive competition. Quality seems to be the first and foremost aspect that is affected adversely by the issues identified in CT workflow. More than half of the participants (59%) who provided free comments think that quality is negatively affected by the CT workflow.
Overall, findings suggest that CT has implications on the translation process, product and the translator. Despite the affordances such as teamwork, peer learning, cooperative competition, the flexibility of volume of work and working time; CT workflow comes with its challenges such as excessive time pressure, aggressive competition, reduced research and self-revision in the translation process, quality compromised for speed, lack of or limited control over the workflow and the final quality of the translation or lack of trust among collaborators. Translators often felt unprepared for the new workflow in terms of briefing or training or simply did not know enough about the functionalities within the platforms. Coupled with the lack of sufficient remuneration, these challenges might contribute to lack of ownership of the translation task as a whole, lack of satisfaction and devaluation of translation in the eyes of the respondents.
In this presentation, we will focus on the selected findings from the survey to highlight the perceived benefits and the most critical challenges that we have identified in the data.
References:
Alfer, Alexa. 2017. “Entering the Translab” In Translation and Translanguaging in Multilingual Contexts - Volume 3, Issue 3, pp. 275-290
Cordingley, Anthony, and Cèline Frigau Manning. 2017. “What is Collaborative Translation?” In Collaborative Translation: From the Renaissance to the Digital Age, ed. by Anthony Cordingley and Cèline Frigau Manning, 1-30. London: Bloomsbury.
CSA Research. 2021. “Collaborative Translation Platforms: The Reality of Sharing Language Projects” (May 2021), Alison Toon and Hélène Pielmeier.
Gough, Joanna, & Katerina Perdikaki. 2018. “Concurrent Translation - Reality or Hype?”. In Translating and The Computer 40, AsLing Proceedings, 79-88. London. 15-16 November 2018.
O’Brien, Sharon. 2011. “Collaborative Translation.” In Handbook of Translation Studies, ed. by Yves Gambier and Luc van Doorslaer, 17- 20. Amsterdam: John Benjamins.
Nimdzi Language Technology Atlas. (Accessed in January 2022) https://www.nimdzi.com/advanced-search/?x=12&y=11&_adv_post_search=atlas
Trzeciak-Huss, Joanna. 2018. “Collaborative Translation”. In Washbourne, K., & Wyke, B.V. (Eds.). The Routledge Handbook of Literary Translation (1st ed.). Routledge. https://doi.org/10.4324/9781315517131
Research Interests:
Linguistically and culturally competent human interpreters play a crucial role in facilitating language-discordant interpersonal healthcare communication. Traditionally, interpreters work alongside patients and healthcare providers to... more
Linguistically and culturally competent human interpreters play a crucial role in facilitating language-discordant interpersonal healthcare communication. Traditionally, interpreters work alongside patients and healthcare providers to provide in-person interpreting services. However, problems with access to professional interpreters, including time pressure and a lack of local availability of interpreters, have led to an exploration and implementation of alternative approaches to providing language support. They include the use of communication technologies to access professional interpreters and volunteers but also the application of various language and translation technologies. This chapter offers a critical review of four different approaches, all of which are conceptualised as different types of human-machine interaction: technology-mediated interpreting, crowdsourcing of volunteer language mediators via digital platforms, machine translation, and the use of translation apps populated with pre-translated phrases and sentences. Each approach will be considered in a separate section, beginning with a review of the relevant scholarly literature and main practical developments, followed by a discussion of critical issues and challenges arising. The focus is on dialogic communication and interaction. Technology-assisted methods of translating written texts are not included.