As students learn to use a particular tool, they should also be encouraged to evaluate that tool in terms of its potential for helping them to complete their task more efficiently. Evaluations can be conducted on a single tool, or comparisons can be made between several competing products. As Otman (1991, 19) and Gouadec (1994, 70) point out, such evaluations may result in suggestions for improvements to the technology (e.g., development of better user interfaces or more flexible storage choices).
In addition, given the range of technologies that are available, students can also learn to assess tools in the light of a particular task or project in order to determine which type of tool can best help them carry out that task (L'Homme 1999a, 343; Sager 1994, 194). This knowledge can, in turn, be used to develop automated diagnostic tools.
1. Examining how tools can change conventional practices
In many professions, and indeed in our everyday lives, technology is called upon to assist us with the tasks that we need to perform. However, it has been observed that technology can sometimes change the very nature of the task that it was designed to facilitate. Ahrenberg and Merkel (1996, 187), Heyn (1998, 135), and Kenny (1999, 71) provide different examples of how CAT tools have affected the nature of translation-related tasks. For example, when recording information on term records, translators no longer record just the base form of the term.
Instead, they record multiple forms of the term so that they can minimize editing by cutting and pasting the appropriate form directly into the target text (see section 4.6.3). Similarly, translators who use translation-memory systems tend to formulate their texts in such a way as to maximize their potential for reuse.
Scherf (1992, 157) and Kenny (1999, 73) note that translator trainers are now in a position to observe students in the immediate translation process and see what approaches and solutions they come up with when faced with the kinds of choices that technology presents to them (e.g., how should they record their terminology). Kenny goes on to contend that the instructors who are providing training in the use of these tools should monitor and report on such decisions with a view to developing new guidelines that take into account the interaction with new technologies.
2. Producing data for empirical investigations
An additional benefit to be gained from introducing technology into the translation curriculum is that a by-product of the use of this technology is the gradual accumulation of data that can be used for other types of studies. For some time, translation theorists (e.g., Holmes 1988; Toury 1980) have been calling for a more empirical basis for their discipline. Electronic corpora and translation memories can provide large quantities of easily accessible data that can be used to study translation. Bilingual parallel corpora (such as those produced through alignment or by using translation-memory systems) can be used to investigate translation strategies and decisions. For instance, Ebeling (1998) has used a bidirectional parallel corpus of Norwegian and English texts to examine the behaviour of presentative English there-constructions as well as the equivalent Norwegian det-constructions in original and translated English and original and translated Norwegian, respectively. Similarly, Maia (1998) has used a bidirectional parallel corpus of English and Portuguese texts to analyze the nature and frequency of the subject-verb-object sentence structure in original and translated English and in original and translated Portuguese.
In addition to being used for research applications, the data generated can be used for pedagogical applications. For example, trainers can build an archive of student translations, which can be used to guide teaching practices. For example, the following types of corpora could be extracted from an archive of student translations.
First, a trainer can extract a text-specific corpus consisting of all translations of a given source text done by the students in a particular class. Using a corpus-analysis tool such as a concordancer, the trainer can then examine corresponding sections of all the translations simultaneously. This allows the trainer to identify areas where the class as a whole is having difficulty, as distinct from problems that may have befallen only one or two students. Spotting patterns of this type is much more difficult and cumbersome when working with separate sheets of paper.
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