The paper discusses a method to triangulate process and product data. We
suggest converting Translog data into a relational format which contains
both process and product data. We outline how this representation allows
us to retrieve and correlate the various dimensions of the data more easily.
The concept of Alignment Unit (AU) is introduced and contrasted with that
of Translation Unit (TU). While AUs refer to translation equivalences in the
source and target texts of the product data, TUs refer to cognitive entities that
can be observed in the process data. With an (almost) exhaustive fragmentation
of the source and target texts into AUs, we are able to distribute and
allocate the entire set of keystroke data to appropriate AUs. Using the properties
of the keystroke data, AUs are quantified in a novel way which enables
us to visualise and investigate the structure of translation production on a
fine-grained scale.
This paper argues that translators can greatly benefit from contrastive studies of discourse structure. Cross-linguistic studies of Italian
and Danish point to significant typological differences in information packaging in the two languages, especially in their use of
deverbalisation. Italian sentences tend to include a larger number of Elementary Discourse Units (EDUs), especially propositions,
than Danish. A higher percentage of these is rhetorically backgrounded by means of non-finite and nominalised predicates. Danish
text structure, on the other hand, is more informationally linear and characterised by a higher number of finite verbs and topic shifts.
These typological differences are transferred into three simple translation rules concerning 1) the number of EDUs, 2) the rhetorical
structure, and 3) the textualisation of rhetorical satellites.