1.5.5 The post-editing competence model of Nitzke, Hansen-Schirra, and Canfora (2019, 2021)

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The post-editing competence model most often cited in present-day literature comes from Nitzke and her colleagues (Nitzke, Hansen-Schirra, et al., 2019), and it is based on the PACTE group’s translation competence model and on Robert’s revision competence model. Nitzke’s proposed model splits post-editing into four core competencies (risk-assessment, strategic, consulting and service competencies) with 8 further subsidiary subcompetencies (see Figure 5).

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Similarly to Rico and Torrejon (2012), Nitzke and her colleagues grant a prominent role to decision-making in post-editing. As a result, one of the key core competencies is “risk-assessment competence”, that is, the ability to identify the advantages and disadvantages of using MT and post-editing in a specific translation setting. The second core competence, “strategic competence”, involves analysing the post-editing brief and forming a strategy to create the required target text in line with the brief. “Consulting competence” refers to the ability to negotiate with clients, project managers and other interested parties in the process about risks, problem-solving strategies, price calculations, etc. Negotiating prices and deadlines appears as part of “service competence”, too, and this core competence also comprises a myriad of skills that help the translator survive in the translation market.

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The core competencies are supported by subsidiary subcompetencies. These are as follows: (1) bilinguals competence, (2) extralinguistic competence (generalized, thematic, and cultural knowledge), (3) instrumental competence (the ability to use CAT tools and other up-to date digital devices), (4) research competence (the ability to find information), (5) revision competence, (6) translation competence, (7) machine translation competence (knowledge about MT systems and how they work) and (8) post-editing competence (skills that enable the post-editor to find and correct errors in the MT-output).

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Nitzke and her colleagues stress the importance of some specific cognitive skills, like a “well-developed ability to concentrate and sustain attention […], stress-resistance, logical reasoning, analytical thinking, and quick-wittedness” (Nitzke, Hansen-Schirra, et al., 2019, 251)
 
Figure 5 Nitzke and Colleagues’ Competence Model
Note. Adapted from Nitzke, Hansen-Schirra, et al. (2019, 250)
 

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Nitzke and Hansen-Schirra published a modified version of their model in 2021. The new model includes approximately the same elements and sub-skills as the 2019 model, but their relations have changed. Overall, the elements in the new model show a more tightly structured organisation. This is also evident in the more elaborate “house” metaphor that guides the new visualisation of the model (see Figure 6).
 
Figure 6 Nitzke and Hansen-Schirra’s Revised Post-Editing Competence Model
Note. Adapted from Nitzke & Hansen-Schirra (2021, 250)
 

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In the revised model, translation competence with its elements forms the foundation of the house. In other words, it functions as an essential basis, without which the house (i.e., post-editing itself) cannot be built. The three pillars of the house comprise error-handling, MT engineering and consulting, among which error-handling appears to be the most important. Error handling can be further divided into error spotting, error classification, and error correction. The three pillars support a roof that symbolises soft skills. Soft skills include a wide range of psycho-physiological components, the post-editor’s self-perception, their affinity towards technological developments and the details of the PE brief.

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An advantage of the revised model is that it is flexible and can be tailored to different job profiles. For example, for post-editors, the error handling pillar is more important than the other pillars; consequently, it must be focused on in training. In contrast, for MT engineers, it is the second pillar (MT engineering) that should be strengthened (Nitzke & Hansen-Schirra, 2021).

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Although the Nitzke model (either version) is far the most frequently cited one in the literature, no comprehensive empirical studies have been built on it. The reason for this could be its complex nature. It is clearly a multicomponential model that intuitively grasps post-editing as a human activity; however, the structure of the model and the relation of the individual elements are somewhat vague. Moreover, it is puzzling how post-editing competence can be an element of post-editing. In light of the issues mentioned here, the present study was not directly based on this model, but some results will be discussed in relation to it, too.

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The models presented in this section are partly theoretically motivated, partly built on observations of the researchers and on previous translation or revision models. Another way to identify skills necessary for post-editing is to ask participants in the language industry. In the next section, the most important empirical studies on pinpointing post-editing skills will be summarised.
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