The power of language and the language of power in M. T. Anderson’s Feed and Gary Stheyngart’s Super Sad True Love Story. A comparative analysis

The aim of my dissertation is to analyze how selected elements of language are addressed in two contemporary dystopias, Feed by M. T. Anderson (2002) and Super Sad True Love Story by Gary Shteyngart (2010). I chose these two novels because language plays a key role in both of them: both are primarily focused on the pervasiveness of technology, and on how the use/abuse of technology affects language in all its forms. In particular, I examine four key aspects of language: books, literacy, diary writing, as well as oral language. In order to analyze how the aforementioned elements of language are dealt with in Feed and Super Sad True Love Story, I consider how the same aspects of language are presented in a sample of classical dystopias selected as benchmarks: We by Yevgeny Zamyatin (1921), Brave New World by Aldous Huxley (1932), Animal Farm (1945) and Nineteen Eighty-Four (1949) by George Orwell, Fahrenheit 451 by Ray Bradbury (1952), and The Handmaid's Tale by Margaret Atwood (1986). In this way, I look at how language, books, literacy, and diaries are dealt with in Anderson’s Feed and in Shteyngart’s Super Sad True Love Story, both in comparison with the classical dystopias as well as with one another. This allows for an analysis of the similarities, as well as the differences, between the two novels. The comparative analysis carried out also takes into account the fact that the two contemporary dystopias have different target audiences: one is for young adults (Feed), whereas the other is for adults (Super Sad True Love Story). Consequently, I also consider whether further differences related to target readers affect differences in how language is dealt with. Preliminary findings indicate that, despite their different target audiences, the linguistic elements considered are addressed in the two novels in similar ways.

Neural Machine Translation in the Field of Law. Analysis and Evaluation against Human Reference of NMT Raw Outputs of an Atto Costitutivo of S.r.l. from Italian into German.

Artificial Intelligence (AI) is gaining ever more ground in every sphere of human life, to the point that it is now even used to pass sentences in courts. The use of AI in the field of Law is however deemed quite controversial, as it could provide more objectivity yet entail an abuse of power as well, given that bias in algorithms behind AI may cause lack of accuracy. As a product of AI, machine translation is being increasingly used in the field of Law too in order to translate laws, judgements, contracts, etc. between different languages and different legal systems. In the legal setting of Company Law, accuracy of the content and suitability of terminology play a crucial role within a translation task, as any addition or omission of content or mistranslation of terms could entail legal consequences for companies. The purpose of the present study is to first assess which neural machine translation system between DeepL and ModernMT produces a more suitable translation from Italian into German of the atto costitutivo of an Italian s.r.l. in terms of accuracy of the content and correctness of terminology, and then to assess which translation proves to be closer to a human reference translation. In order to achieve the above-mentioned aims, two human and automatic evaluations are carried out based on the MQM taxonomy and the BLEU metric. Results of both evaluations show an overall better performance delivered by ModernMT in terms of content accuracy, suitability of terminology, and closeness to a human translation. As emerged from the MQM-based evaluation, its accuracy and terminology errors account for just 8.43% (as opposed to DeepL’s 9.22%), while it obtains an overall BLEU score of 29.14 (against DeepL’s 27.02). The overall performances however show that machines still face barriers in overcoming semantic complexity, tackling polysemy, and choosing domain-specific terminology, which suggests that the discrepancy with human translation may still be remarkable.