With reference to practical translation studies, as corpora can be used to raise linguistic and cultural awareness in general (cf. Bernardini, 1997; Hunston, 2002: 123), they provide a useful and effective reference tool and a workbench for translators and trainees. In this respect even a monolingual corpus is helpful. Bowker (1998), for example, found that corpus-aided translations were of a higher quality with respect to subject field understanding, correct term choice and idiomatic expressions than those undertaken using conventional resources. Bernardini (1997) also suggests that traditional translation teaching should be complemented with large corpora concordancing (LCC) so that trainees develop 'awareness', 'reflectiveness' and 'resourcefulness', the skills that 'distinguish a translator from those unskilled amateurs'.
In comparison to monolingual corpora, comparable corpora are more useful for translation studies. Zanettin (1998) demonstrates that small comparable corpora can be used to devise a 'translator training workshop' designed to improve students' understanding of the source texts and their ability to produce translations in the target language more fluently. In this respect, specialised comparable corpora are particularly helpful for highly domain-specific translation tasks, because when translating texts of this type, as Friedbichler and Friedbichler (1997) observe, 'the translator is dealing with a language which is often just as disparate from his/her native language as any foreign tongue'. Several studies show that translators with access to a comparable corpus with which to check translation problems 'are able to enhance their productivity and tend to make fewer mistakes' (Friedbichler & Friedbichler, 1997) when translating into their native language. When translation is from a mother tongue into a foreign language, 'the need for corpus tools grows exponentially and goes far beyond checking grey spots in L1 language competence against the evidence of a large corpus' (Friedbichler & Friedbichler, 1997). For example, Gavioli and Zanettin (1997) demonstrate how a very specialised corpus of texts on the subject of hepatitis helps to confirm translation hypotheses and suggest possible solutions to problems related to domain-specific translation.
While monolingual and comparable corpora are of use to translation, it is difficult to generate 'possible hypotheses as to translations' with such data (Aston, 1999). Furthermore, verifying concordances is both time-consuming and error-prone, entailing a loss of productivity. Parallel corpora, in contrast, provide '[g]reater certainty as to the equivalence of particular expressions', and in combination of suitable tools (e.g. ParaConc), they enable users to 'locate all the occurrences of any expression along with the corresponding sentences in the other language' (Aston, 1999). As such, parallel corpora can help translators and trainees to achieve improved precision with respect to terminology and phraseology and have been strongly recommended for these reasons (e.g. Williams, 1996). A special use of a parallel corpus with one source text and many translations is that it can offer a systematic translation strategy for linguistic structures which have no direct equivalents in the target language. Boyse (1997), for example, presents a case study of the Spanish translation of the French clitics en and y,where the author illustrates how a solution is offered by a quantitative analysis of the phonetic, prosodic, morphological, semantic and discursive features of these structures in a representative parallel corpus, combined with the quantitative analysis of these structures in a comparable corpus of L1 target language. Another issue related to translator training is translation evaluation. Bowker (2001) shows that an evaluation corpus, which is composed of a parallel corpus and comparable corpora of source and target languages, can help translator trainers to evaluate student translations and provide more objective feedback.
Finally, in addition to providing assistance to human translators, parallel corpora constitute a unique resource for the development of MT systems. Starting in the 1990s, the established methodologies, notably the linguistic rule-based approach to MT, have been challenged and enriched by an approach based on parallel corpora (cf. Hutchins, 2003: 511; Somers, 2003: 513). The new approaches, such as example-based MT (EBMT) and statistical MT, are based on parallel corpora. With EBMT, for example, a new input is matched against the database of already translated texts to extract suitable examples which are then combined to generate the correct translation (see Somers, 2003; Hutchins, 2003). As well as automatic MT systems, parallel corpora have also been used to develop CAT tools for human translators, such as translation memories (TM), bilingual concordances and translator-oriented word processor (cf. Somers, 2003; Wu, 2002).
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