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.

Stratigraphic study and analysis of the barchan dunefield of the Laguna coastal zone in Santa Catarina, Brazil

The association of several favorable factors has resulted in the development of a wide barchan dune field that stands out as a fundamental element in the coastal landscape of southern Santa Catarina state in Brazil. This original ecosystem is being destroyed and highly modified, due to urbanization. This work identifies and discusses its basic characteristics and analyzes the favorable factors for its preservation, in the foreseen of both a sustainable future and potential incomes from ecotourism. The knowledge of the geologic evolution allows to associate this transgressive Holocene dunes formation to more dissipative beach conditions. Spatial differences on morphodynamics are related to local and regional contrasts in the sediment budget, with an influence on gradients of wave attenuation in the inner shelf and consequently with influence in the level of coastal erosion. The link between relative sea level changes and coastal eolian sedimentation can be used to integrate coastal eolian systems to the sequence stratigraphy model. The main accumulation phase of eolian sediments would occur during the final transgressive and highstand systems tracts. Considering the global character of Quaternary relative sea level changes, the Laguna transgressive dune field should be correlated with similar eolian deposits developed along other parts of the Brazilian coast compatibles with the model of dunefield initiation during rising and highstand sea level phases.