Translation of Metaphors in Official and Automatic Subtitling and MT Evaluation
DOI:
https://doi.org/10.33919/JCAL.23.1.4Keywords:
Metaphor, machine translation, MT evaluationAbstract
One of the main aims of this work is to compare and analyse the translation of metaphors in subtitles as performed by human translators and by machine translation, and conduct MT evaluation. The work considers two YouTube videos of a Cyberpunk 2077 (2020) videogame walkthrough. The first video is in the original language (English) with English subtitles and the second one is an officially translated video in Russian, with Russian subtitles. Both videos have the same content, but in different languages. Metaphors were extracted manually from selected audiovisual material in English by the usage of MIPVU (Metaphor Identification Procedure Vrije Universiteit). In order to achieve our aims, first the translation of these metaphors in the official Russian subtitles were analysed; secondly, their automatic translation into Russian as it appears on YouTube by Google Translate were analysed as well; after that the results were compared to find the similarities and the differences between the automatically translated version of the metaphors on YouTube and the translated metaphors in the official subtitling. Another aim is to perform Machine Translation (MT) evaluation using the BLEU (Bilingual Evaluation Understudy) algorithm and to determine the errors made by MT while translating metaphors in the analysed subtitles. Three examples, which were taken from the videos, are presented in the format of cases. The cases show different metaphors and the situations they were used in and analyse why these metaphors were used in that particular situation, how metaphors were identified there, how they were translated and why they were translated exactly in this way. Furthermore, the machine translation of the same metaphors is analysed and a comparison between them is made. The topic of the speech recognition process and the metaphor identification procedure is also touched upon. The results demonstrate that although machine translation is able to translate frequently used, popular metaphors, or metaphors, the literal translation of which retains the meaning, it is still difficult for the machine to recognise original author’s metaphors or to translate using the context of the situation. The results could encourage training the machine to recognize metaphors and to create a larger database of metaphors to identify them.
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