"Human Translation Quality Estimation and Translationese"

by Dr Maria Kunilovskaya, University of Wolverhampton

Update: the event has now finished (Nov 5th 2021).

Abstract

In the first part of the talk I will present a fairly novel NLP task of human translation quality estimation (HTQE) and discuss problems associated with benchmarking human translation quality. How far do human assessors agree on (human) translation quality? What types of labels/scores can be used to reflect quality? What are the existing approaching to predict these labels? If a professional jury in a translation contest manages to achieve agreement on the top-ranking and, especially on bottom-ranking, translations (with possible fine-grained disagreements about the exact ranks) what does it take to teach a machine to distinguish between good and bad translations? Such a model can be applied in educational and certification contexts for filtering out translations that are definitely below the expected standard to reduce the workload for human assessors. The second part of the talk will explore the concept of translationese, and its potential for learning human translation quality. Do you expect good translations to read smoothly and naturally as if originally-written in the target language? Can we use the distance between translations and the expected target language norm to measure translation quality? I will largely draw on the findings reported in our latest publications:

References:

  • Kunilovskaya, M. and G. Corpas Pastor (2021). Translationese and register variation in English-to-Russian professional translation. In L. Lim, D. Li, and V. Wang (Eds.), New Perspectives on Corpus Translation Studies. Springer.
  • Kunilovskaya, M., Lapshinova-Koltunski, E., & Mitkov, R. (2021).Translationese in Russian Literary Texts. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. EMNLP.

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