Constructing a Parallel Corpus

The corpus requires special apparatus (such as computer terminals, computer programmes or bespoke software) that need to be described as a part of the strategy for conducting research (ibid: 82). In this research, I have chosen three software packages to evaluate these hypotheses: namely, UltraEdit, Microsoft Excel and Paraconc to work out the specific research results.

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UltraEdit’s text editing features make editing lists and columns an intuitive experience, not the exercise in tedium it used to be. With features like multi-caret editing, column/block editing and multi-select, it is a simple text editor when I want it to be and a multi-cursor power editor when you need it to be. Since UltraEdit can identify all kinds of subtitle files, it is suitable for this research. The use of this software is simple: when you drag any kind of the above-mentioned type of subtitle file to the UltraEdit, the bilingual subtitle pairs appear with automatically labelled serial numbers. Then I only need simple arithmetic skills to obtain the total number of subtitle pairs in a film. What is more, when I select and click on a particular line of subtitle, UltraEdit can tell the numbers of words in this specific line. Since the first hypothesis in this research is about the testing of the length difference between the translated subtitles and their corresponding original subtitles, UltraEdit can help to work out each line of subtitle first, then put these data into Microsoft Excel, such that the result of length difference can be calculated by a manual set-up format such as “A-B=X”.

Meanwhile, Microsoft Excel is arguably the most suitable tool for collating statistics amongst commonly used computer software. In order to assemble all the language data from the selected 15 films, Excel will naturally be the appropriate option for this activity. Moreover, its feature of “properties” helped me tailor Excel to meet her own specific requirements in the collation of statistics. For example, in order to calculate the length difference between the original English text and the subtitles translated into Chinese, I am able to create “remark I” with the name of “the length difference” in one additional column and use the preset formula as “sub (A, B)” (sub is one of the preset functions in Excel) to get the desired result. A more detailed description of the use of Excel in the collection of data in the corpus of this study is given in the following section of this chapter. Inevitably, since the research is concerned with E-C subtitling, the main limitations and appropriate translation strategies consequently become the key investigating objectives. According to the classification of the main types of corpora in translation studies, a parallel corpus that compares the original text with the translated subtitles is the most suitable. Therefore, Microsoft Excel and UltraEdit can help me to test the first hypothesis about the length difference as well as being a preparation to the next step of testing the second and third hypotheses about the use of culturally loaded expressions in Chinese translated subtitles.

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Lastly, the software of ParaConc is “a bilingual or multilingual concordancer that can be used in contrastive analyses, language learning, and translation studies/training” (http://www.paraconc.com/). In this research, I plan to test the frequency of the use of traditional expressions as well as the frequency of the use of popular expressions in the E-C animation films’ subtitle translation. The results of these frequencies can help me to identify my second and third hypotheses concerning whether the use of these two types of expressions can make the translated subtitles of English animation films more attractive among Chinese audiences and among young people in particular.

After identifying the most suitable software to build up the specific corpus for this research, the detailed application procedures are as follows:

a) Chosen Linguistic Data

The chosen texts constitute the first element in a corpus. In this study, 15 English language texts and their corresponding Chinese language subtitles are brought together in a parallel corpus as the linguistic data. These data are classified respectively in terms of the types of traditional expressions and popular expressions.

b) Collecting Data

Before establishing the corpus, with the recommendation by Huang, I had the chance to talk with two scholars, Liang and Xu, during a summer course I attended in 2014. These two scholars are well-known professionals in the field of corpus study in China.

In the course of the conversation about how to construct a suitable corpus for this specific study, they offered a great deal of useful advice that enabled the researcher to build up her corpus in a purposeful and focused way. The main elements of this advice were as follows: firstly, they encouraged the researcher to seek practical input from the Shanghai Translated Film Studio, the official company for translating exported films in China; secondly, they recommended naming this specific corpus as a “self-built parallel corpus for a special purpose”, in view of the usage and scale of the corpus; thirdly, in the next phase of the research work, they encouraged the researcher to continue inputting more data into the corpus in order to obtain more

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effective and objective data results; lastly, they recommended transforming the data results into value measurements with a practical application to help real-world subtitlers produce subtitles of a more effective and reliable quality.

Given that the research objective is animation film subtitles, most of the raw linguistic data are spoken language rather than written language. Therefore, the source of these raw language materials is the films’ dialogues. Yang and Tang (2014: 174) advised to download all the relevant materials from the Sheshou website. This website is free of charge and offers bilingual subtitles (English and Chinese) of almost all the animated films released in China in the last 15 years or so. Therefore, the first raw materials for this research were the downloaded text-based film dialogues. When I inputted the names of the films on the website, specifying “E-C bilingual subtitles” as the key search request, she obtained two main subtitle categories from this website. One category was originally in PDF or CAJ format, while the other was in SSA or SRT format. In the search of the 15 selected film subtitles, most of them are in SRT format; I needed to transfer this format into plain text versions. Then, the previously introduced UltraEdit helps to open each type of the mentioned format of subtitle documents as the following example shows:

Screenshot 2

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The alignment of dual languages in a corpus is useful to researchers as an efficient means of collecting data. This technology helps researchers align bilingual materials with the same meaning in the source and translated texts with its algorithm. The units of alignment are varied: namely, passage alignment, sentence alignment, phrase alignment, word alignment and even character alignment. The smaller the unit is, the most linguistic the information it offers. However, in this research project, because I want to verify my study hypotheses about the impact of cultural elements in subtitling, I have chosen to undertake sentence aligning. Sentence aligning can help me to observe the length difference between the source subtitle and the target subtitle. As the above UltraEdit sample shows, after the aligning of each pair of subtitles in the unit of sentence is accomplished, the use of Microsoft Excel can help to fulfil the further calculation. Moreover, the aligning in the sentence unit can also help me to test the frequency of the use of two kinds of culturally loaded expressions in my second and third hypotheses; therefore, the use of UltraEdit in this research is an appropriate choice. Besides, as a way of preparation and backup, I can also keep the raw materials in the form of “time code + original English original subtitle + Chinese translated subtitle” in 15 UltraEdit files as well. The advantages of taking this preparatory step are as follows: the time code helped the researcher to self-evaluate after finishing the task; I could check any pair or pairs of bilingual subtitles in terms of their synchronism, which means that only the frames match the subtitles, resulting in the audience being able to see the synchronism of pictures and dialogues. Then, putting the translated subtitles and the source subtitles into two lines, as well as locating them in the form of “text-align left”, helped the researcher to see the length difference between each pair immediately. The edition of Microsoft Excel in the corpus involves 15 files, each of which contains one particular set of film subtitles. The other edition of the Word document in the corpus also involves 15 files, as well as backup files separately containing the 15 selected film subtitles.

The following examples show a sample is kept in three kinds of software: the UltraEditor, Excel and Notepad:

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(UltraEdit)

(Excel)

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Screenshot 5

Take the third pair of subtitles as an example. The time code shows that the dialogue started at the first minute and 31 seconds of the film, with the first subtitle lasting about two seconds, while there are seven words in the original subtitle and three characters in the translated subtitle. In a visual sense, because the structure of Chinese characters is relatively compact and independent, the length difference between the bilingual subtitles is not very obvious. But because the different word formations, Chinese characters look more complex than English characters, therefore, I estimate the reading time of Chinese subtitles should be longer than that of English ones.

As the description above explains, the software of UltraEdit and Microsoft Excel can help me realize my testing of the first hypothesis, that is, the length comparison between each pair of Chinese translated subtitles and its corresponding English original text. Then, in order to test and calculate the frequency of two typical expressions in Chinese translated subtitles, that is, traditional expression and popular expression, another two hypotheses need to be tested, which concern whether these two types of expressions are effective in making the translated subtitles more attractive and interesting to audiences, and to identify the strategies used behind these

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expressions. In the process, another software package “paraconc”, is available to assist me in obtaining results about the “frequency” of each type of expression in the collected data. As the following example shows, I put the label “TE” (traditional expression) and “PE” (popular expression) at the end of each translated subtitle which is relevant to traditional expression or/and popular expression, then put the whole text of one film’s data into paraconc; the subtitles which are labelled will be shown in serial numbers.