Translationese: Between Human and Machine Translation

Translated texts, in any language, have unique characteristics that set them apart from texts originally written in the same language. Translation Studies is a research field that focuses on investigating these characteristics. Until recently, research in machine translation (MT) has been entirely divorced from translation studies. The main goal of this tutorial is to introduce some of the findings of translation studies to researchers interested mainly in machine translation, and to demonstrate that awareness to these findings can result in better, more accurate MT systems. First, we will survey some theoretical hypotheses of translation studies. Focusing on the unique properties of translationese (the sub-language of translated texts), we will distinguish between properties resulting from interference from the source language (the so-called “fingerprints” of the source language on the translation product) and properties that are source-language-independent, and that are presumably universal. The latter include phenomena resulting from three main processes: simplification, standardization and explicitation. All these phenomena will be defined, explained and exemplified. Then, we will describe several works that use standard (supervised and unsupervised) text classification techniques to distinguish between translations and originals, in several languages. We will focus on the features that best separate between the two classes, and how these features corroborate some (but not all) of the hypotheses set forth by translation studies scholars. Next, we will discuss several computational works that show that awareness to translationese can improve machine translation. Specifically, we will show that language models compiled from translated texts are more fitting to the reference sets than language models compiled from originals. We will also show that translation models compiled from texts that were (manually) translated from the source to the target are much better than translation models compiled from texts that were translated in the reverse direction. We will briefly discuss how translation models can be adapted to better reflect the properties of translationese. Finally, we will touch upon some related issues and current research directions. For example, we will discuss recent work that addresses the identification of the source language from which target language texts were translated. We will show that native language identification (in particular, of language learners) is a closely related task to the identification of translationese. Time permitting, we will also discuss work aimed at distinguishing between native and (advanced, fluent) non-native speakers.