Forschungskolloquium

Forschungskolloquium des FB 3

Dienstags ab 18.05 Uhr 

per Videokonferenz (abwechselnd mit dem Kolloquium des IdSL)

Koordination: Maria Wünsche und Dr. Marco Agnetta

Programm im Sommersemester 2020

21.04.2020 Ekaterina Lapshinova-Koltunski Translationese Features as Indicators of Quality in English-Russian Human Translation
05.05.2020 Vortragende_r Titel
19.05.2020 Vortragende_r Titel
09.06.2020 Vortragende_r Titel
23.06.2020 Linda Stegmann Übertitelung per Smartglasses für gehörlose und schwerhörige Personen
07.07.2020 Vortragende_r Titel
An einem Vortrag Interessierte können sich bitte bei Dr. Agnetta (agnetta@uni-hildesheim.de) melden.

Aktueller Vortrag: Translationese Features as Indicators of Quality inEnglish-Russian Human Translation

Dienstag, 21.04.2020, 18.05 Uhr per Videokonferenz: 

Vortragende: Ekaterina Lapshinova-Koltunski, Titel: Translationese Features as Indicators of Quality in English-Russian Human Translation

Abstract: We use a range of morpho-syntactic features inspired by research invariational linguistics and translation studies to reveal the association between translationese and human translation quality. Translationese is understood as any statistical deviations of translations from non-translations (Baker, 1993) and is assumed to affect the fluency of translations, rendering them foreign-sounding  and clumsy of wording and structure. This connection is often positedor implied in the studies of translationese or translational varieties, but is rarely directly tested. Our 45 features include frequencies of selected morpho-logical forms and categories, some types of syntactic structures and relations, as well as several overall text measures extracted from Universal ependencies annotation. The research corpora include English-to-Russian professional and student translations of informational or argumentative newspaper texts and a comparable corpus of non-translated Russian.  Our results indicate lack of direct association between translationese and quality in our data: while our features distinguish translations and non-translations with the near perfect accuracy, the performance of the same algorithm on the quality classes barely exceeds the chance level.