In the world of Descriptive Translation Studies, translationese refers to the specific traits that characterise the language used in translations. While translationese has been often investigated to illustrate that translational language is different from non-translational language, scholars have also proposed a set of hypotheses which may characterise such di erences. In the quest for the validation of these hypotheses, embracing corpus-based techniques had a well-known impact in the domain, leading to several advances in the past twenty years. Despite extensive research, however, there are no universally recognised characteristics of translational language, nor universally recognised patterns likely to occur within translational language. This thesis addresses these issues, with a less used approach in the eld of Descriptive Translation Studies, by investigating the nature of translational language from a machine learning perspective. While the main focus is on analysing translationese, this thesis investigates two related sub-hypotheses: simplication and explicitation. To this end, a multilingual learning framework is designed and implemented for the identification of translational language. The framework is modelled as a categorisation task, the learning techniques having the major goal to automatically learn to distinguish between translated and non-translated texts. The second and third major goals of this research are the retrieval of the recurring patterns that are revealed in the process of solving the task of categorisation, as well as the ranking of the most in uential characteristics used to accomplish the learning task. These aims are ful lled by implementing a system that adopts the machine learning methodology proposed in this research. The learning framework proves to be an adaptable multilingual framework for the investigation of the nature of translational language, its adaptability being illustrated in this thesis by applying it to the investigation of two languages: Spanish and Romanian. In this thesis, di erent research scenarios and learning models are experimented with in order to assess to what extent translated texts can be diff erentiated from non-translated texts in certain contexts. The findings show that machine learning algorithms, aggregating a large set of potentially discriminative characteristics for translational language, are able to diff erentiate translated texts from non-translated ones with high scores. The evaluation experiments report performance values such as accuracy, precision, recall, and F-measure on two datasets. The present research is situated at the con uence of three areas, more precisely: Descriptive Translation Studies, Machine Learning and Natural Language Processing, justifying the need to combine these elds for the investigation of translationese and translational hypotheses.

Den här studien behandlar fenomenet tolkspråk – tanken om att tolkat språk skiljer sig från icke-tolkat språk. Översättningsvetenskapen och dess motsvarighet översättarspråk har utgjort en stor inspirationskälla till arbetet, då forskningen kommit längre där. Ett forskningsområde behandlar så kallade översättningsuniversalier – universella regler eller lagar för hur översatt språk ser ut. En av dessa lagar kallas för normalisering. I studien undersöks om normalisering är applicerbart även på teckenspråkstolkning, med utgångspunkt i påståendet att normalisering kan vara synligt i översatt text som en överrepresentation av typiska målspråksdrag. För att undersöka detta har två jämförbara korpusar använts, dels Svensk teckenspråkskorpus (SSLC) med icke-tolkade teckenspråkstexter, dels Korpus för simultantolkade teckenspråkstexter (KST) med tolkade teckenspråkstexter. Det typiska, teckenspråkiga drag som valts för undersökningen är det tecken som glosas KOPPLA. Förekomsten av tecknet i de båda korpusarna har undersökts för att kunna upptäcka en eventuell överrepresentation i KST. Resultaten visar att KOPPLA mycket riktigt är överrepresenterat i KST, men att det är svårt att generalisera på grund av flera begränsande faktorer. / This essay is concerned with the subject of interpretese – the idea that interpreted language differs from non-interpreted language. Within translation studies, the corresponding term is translationese, and this essay draws upon much of the research in this field, as it is more developed. One particular area of research into translationese revolves around so called translation universals, or universal features of translation. They could be described as rules or laws that define translated language. One of those universals is called normalisation. This essay seeks to answer if normalisation also exists in Sign Language interpreting, with reference to exaggeration of typical target language patterns. Two comparable corpora were used, Swedish Sign Language Corpus (SSLC) with non-interpreted Sign Language texts, and Korpus för simultantolkade teckenspråkstexter (KST) with interpreted Sign Language texts. The typical target language pattern that was chosen for the investigation is the Swedish sign KOPPLA. Instances of the sign were investigated in both corpora, to spot any exaggeration in KST. The results show that KOPPLA is in fact overrepresented in KST, but that one should be careful to generalize, as several limiting factors were at play.

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