Developing phraseological competence in L2 legal translator trainees: a proposal of a data mining technique applied in translation from an LLD into ELF

ABSTRACT

Evidence from the contemporary translation services market and many centuries of translation practice demonstrate that translation into a non-native language (L2 translation) can be performed effectively, despite the once-strong resistance to it on the grounds of it being perceived as unprofessional and inherently deficient. L2 translation is in fact unavoidable in the case of so-called languages of low diffusion, the command of which happens to be rather limited among native speakers of major languages. However, although the academic dispute about the validity of L2 translation seems decidedly milder now, there is still a lacuna within L2 translator training that needs to be addressed. This paper indicates that what usually betrays an L2 translation is its phraseological profile, often recognised as unnatural by native speakers of the target language. The aim of this paper is to propose a corpus-based data mining technique that may help L2 legal translator trainees become more observant with regards to phraseological patterning of foreign legal discourse, and more self-confident in taking well-informed translation decisions.

KEYWORDS:

• L2 translation
• legal translator training
• phraseological competence
• corpora in translator education
• language of limited diffusion
• directionality in translation

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. Apparently, it was Erich Prunč (2000, 2003) who first used the notion of suboptimality in reference to L2 translation. Prunč focuses on the phenomenon of so-called calculated suboptimality as an acceptable solution in particular L2 situations (2003, 83). Other authors speak briefly of ‘suboptimal quality in L2 translation’ (Pavlović 2007, 81, discussing Newmark’s approach to L2 translation), ‘suboptimal translation performance’ (Delmonte 2013, 135, discussing machine translation), ‘suboptimal translation solutions’ (Krüger 2015, 418, discussing translation shifts), although they do not provide detailed explanations on how suboptimality should be defined in the context of translation.

2. For the assignment of corpora to the development of specific competences within particular models, see Biel (2017, 4–5).

3. For more detailed information on available legal corpora, see Biel (2017, 3–4), and Borja Albi (2019).

4. However, other names for this type of corpus have also been used, e.g. purpose-built (predominantly in ELT and EAP contexts, e.g. Thompson and Tribble 2001; Hüttner 2010). For more names, see Corpas Pastor and Seghiri (2009, 78).

5. The term formal correspondent is used here in Catford’s definition, to denote ‘any TL category which may be said to occupy, as nearly as possible, the “same” place in the economy of the TL as the given SL category occupies in the SL’ (1965, 32), in opposition to textual equivalent, which is defined as ‘any TL text or portion of text which is observed on a particular occasion (…) to be the equivalent of a given SL text or portion of text’ (27) (my emphasis).

6. The ongoing InLeTra project (Giczela-Pastwa 2019) is focused on analysing phraseological discrepancies between L2 English translations of Polish legal acts and non-translated legislation in English. The methodology of choice is the comparable-parallel corpus method (Biel 2016). One of the corpora used in the project consists of twenty-seven L2 English translations of selected Polish legal acts.

7. Cambridge includes only the derivative aforementioned. However, the high position of mention in resources which prioritise synonyms (i.e. first in OXFORD and Macmillan, second in Collins) makes this verb the first choice in further exploration. In the case of OXFORD, mention is given in bold as the word closest in meaning to the entry word, i.e. refer in its first meaning.

8. Due to space constraints, only the first of the words is discussed in this paper. Nevertheless, the proposed data mining technique can be successfully applied in order to determine an equivalent of the other word, too.

9. Similarly as in Example (2) above, only the first of the words is discussed in the paper.