A Critical Assessment of Universals of Translation: in the Light of Corpus-Based Approach

Topics: Translation, Source text, Machine translation Pages: 10 (3386 words) Published: August 24, 2013
Translation and Interpreting Studies I
A Critical Assessment of Universals of Translation: In the light of corpus-based approach

The 1990s witnessed the rapid development of the corpus-based approach to translation studies. According to Laviosa (2010: 80), “a corpus is a collection of authentic texts held in electronic forms assembled according to specific design criteria”. It is of great interest to apply corpora to all sorts of disciplines due to the fact that corpora can be used to access vast quantities of data and prepared for computer processing. Baker (1993: 243) first put forward the corpus-based methodology to translation studies and since then, numerous translation scholars have attempted to identify the nature of translated texts by carefully examining the product and process of translation. Thus, various features of translated texts are observed and summarized, forming the basis for universals of translation which is defined by Baker (1993: 243) as “features typically occur in translated texts rather than original utterances and which is not the result of interference from specific language systems”. Frawley (1984) regards these features as the ‘third code’ of translation, which differs from both the source language and the target language. This paper first takes a brief look at typologies of corpus frequently used in translation studies and then moves on to examine the four potential universals of translation, namely, simplification, explicitation, normalization and leveling out, which are proposed by Baker on the basis of both theoretical statements and non-corpus investigations of translated texts (Olohan 2004: 91).

Types of corpus in translation studies
As the definition of corpus indicates, all the texts compiled in a corpus are based on explicit design criteria, which are divided by Laviosa (2002: 34) into four hierarchical levels. The first level consists of six sets of contrastive parameters which form the most general features of a corpus, such as language types (English, German, French etc.) and text types (written, spoken or mixed). The subsequent levels are made up of parameters that are specific to a corpus designed for translation studies, such as single vs. comparable or parallel and mono-source-language vs. bi-source-language or multi-source-language. According to Baker (1995: 230), corpus designed specifically for translation research mainly includes three types, namely, parallel corpus, multilingual corpus and comparable corpus. Parallel corpus, consisting of source texts and their translated versions, can be applied in various occasions, including translation training and machine translation. Take Google Translate (GT) for example, at present, GT is able to offer bidirectional translation between 58 languages inasmuch as it has a powerful pre-existing parallel corpus (Bellos 2011). Probably, the best known corpus of this type is the English-French Hansards Corpus in Canada (Baker 1995: 231). Multilingual corpus is made up of two or more monolingual corpora in different languages and all the texts included are original writings as opposed to translated ones. Thus, multilingual corpus can be used to investigate natural patterns of languages and therefore play a crucial role in materials writing, translator training and improving machine translation systems (Baker 1995). Comparable corpus consists of two separate corpora in the same language: one contains original writings while the other contains translated texts. In order to fully reflect the comparability between the corpora, the two sets of texts should, as Baker (1995) states, “cover a similar domain, variety of language and time span, and be of comparable length.” Besides, the original authors and translators of the texts compiled in the translational corpus should be representative. All the requirements mentioned above ensure that comparable corpus can be used to investigate features of translated texts regardless...

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