  Machine translation has an undesirable propensity to produce "translationese"
artifacts, which can lead to higher BLEU scores while being liked less by human
raters. Motivated by this, we model translationese and original (i.e. natural)
text as separate languages in a multilingual model, and pose the question: can
we perform zero-shot translation between original source text and original
target text? There is no data with original source and original target, so we
train sentence-level classifiers to distinguish translationese from original
target text, and use this classifier to tag the training data for an NMT model.
Using this technique we bias the model to produce more natural outputs at test
time, yielding gains in human evaluation scores on both accuracy and fluency.
Additionally, we demonstrate that it is possible to bias the model to produce
translationese and game the BLEU score, increasing it while decreasing
human-rated quality. We analyze these models using metrics to measure the
degree of translationese in the output, and present an analysis of the
capriciousness of heuristically-based train-data tagging.

Computation and Language