Using Machine Translation for Translation and Audiovisual Translation, a Casestudy

For my master’s thesis, I carry out research on using machine translation for both the

translation of text and audiovisual translation. The aim of this research is to explore, describe, and

evaluate this translation tool to find an answer to the main research question: is machine translation

applicable to autonomously translate texts and audiovisual segments? By combining three different

methods – a literary review, a practical experiment, and an observation during my internship – the

aim is to implement methodological triangulation to be able to come to a conclusion that is wellfounded.

To examine machine translation for text and audiovisual content a division is made between

these two translation branches to properly carry out in-depth research per branch which is then

brought together to compare the results and draw a conclusion.

By comparing how machine translation is used for the two translation branches mentioned

above, it quickly becomes clear that the tool works in very different ways, depending on what kind of

content needs translating. This is because for the audiovisual translation an extra layer of complexity

is added as spoken language needs to be converted into written language before translation whereas

for the translation of written language this is not applicable. As it is a more complicated matter,

scientists have only started investigating it about thirty years ago even though the first research on

using machine translation for text stems from the 1930s. In addition to this, not many scientists have

ventured to do research on automated audiovisual translation which is why this practice is still

underdeveloped and very few sources can be found that show practical findings. This lack of research

and development makes using machine translation for audiovisual translation a very sophisticated

endeavour.

Not only the way machine translation works for the different translation branches differs, but

also the mistakes that can be found in its output. When evaluating the machine translation of text the

following main issues were found: consistency, meaning, and fluency. For the audiovisual translation,

the tool had more problems in regard to interpreting the spoken language correctly and completely,

and producing well-written and grammatically correct subtitles. This was mainly due to the nature of

spoken language, which is more colloquial and often contains linguistic structures that are not

acceptable in written language and therefore need to be rephrased in the subtitles. In general,

machine translation seemed to have issues in both translation branches with producing well-written

language that could have been written by a native speaker. All these mistakes machine translation

tools tend to commit show that machine translation can to this day not be used autonomously and

the interference of a professional and trained translator is necessary to guarantee a translation that

is qualitative.