Measuring Sentiment Bias in Machine Translation
Kai Hartung Aaricia Herygers Shubham Kurlekar Khabbab Zakaria Taylan Volkan Sören Gröttrup Munir Georges
Biases induced to text by generative models have become an increasingly large
topic in recent years. In this paper we explore how machine translation might
introduce a bias in sentiments as classified by sentiment analysis models. For
this, we compare three open access machine translation models for five
different languages on two parallel corpora to test if the translation process
causes a shift in sentiment classes recognized in the texts. Though our
statistic test indicate shifts in the label probability distributions, we find
none that appears consistent enough to assume a bias induced by the translation
process.