Writing across Languages

Initial thoughts on converting & annotating multilingual sentences + my experiences with examples from Yvonne Adhiambo Owuor’s „Dust“

From a literary translation point of view, I am very interested in the work with multilingual anglophone texts as it is something that I will probably come across quite a bit in the actual work as a translator of novels from English to German. The seminar „Writing Across Languages: The Post-Monolingual Anglophone Novel“ deals precisely with this topic in the context of the digital humanities. And at first, digital humanities didn’t sound too overwhelming to me. Because like most students, I work with a laptop every day.

When it came to actually converting and annotating some multilingual sentences, and hearing words such as programming language, the visualization of data and code during the preparation though, I have to admit I developed quite a trepidation. After all, one of the reasons I decided to work with texts was because I try to stay away from advanced technology that goes beyond the surface as far as I possibly can and when I have problems with my technical devices, I’d rather ask someone who is familiar with it, than despair on my own. So, I was very relieved that we neither had to write code ourselves nor were we required to have any previous experience in the field. What really helped me was that we worked with an example sentence all together in class. This way, it did not feel as scary, since I only had to follow the instructions I was given with immediate feedback and any issues that occurred could be solved together immediately. So, there was no need to despair. One problem that did occur with the first example sentence was, when copying the sentence into the cell, there need not be any paragraphs, otherwise the code will not work. If I had encountered the problem on my own, I am sure it would have been much more difficult and also time-consuming to find out what I had done wrong (or whether I had broken something – deleted the whole internet, who knows).

After this first shared experience it was much less of an effort to get started on my own. And it was even fun to start experimenting with the sentences from the novel „Dust“ that I had read in preparation and to check, what mistakes the English-based code causes. The errors I came across the most were the categorizations of many of the non-English words (in the case of the novel „Dust“ by Yvonne Adhiambo Owuor these are words from Kiswahili, Latin, Spanish and a local variety of English) as proper nouns and nouns in the cell for the part of speech and as compounds in the cell concerned with the dependency encoding. This tendency is especially strong, if several non-English words follow each other or at least in these cases it becomes most obvious for the observer because the annotation makes it look like there are whole sentences just consisting of proper nouns and nouns without any verbs. The encoding of the dependency also seemed to be quite arbitrary. In some cases, tokens that were far removed from each other and seemingly were not connected to each other (including punctuation) were categorized as dependent. Punctuation was another issue, especially in the visualization of the dependencies. Usually, when the first token was an inverted comma, it was also visualized as a single token in the dependency tree. With the other punctuations this was not the case, they got assigned to another token and these two tokens where then depicted as one. The merging of tokens also seemed quite arbitrary because, especially with non-English words, sometimes the word in front of the punctuation and sometimes the word following it were merged with the punctuation token.

Now that I have dealt with some examples myself, I am very interested to see what mistakes the others found with their example sentences and what the next steps of the analysis of our findings will look like!

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