Biases induced to text by generative models have l become an increas-ingly large topic in recent years. In this paper we explore how machine translationmight introduce a bias in sentiments as classified by sentiment analysis models. For this, we compare three open access machine translation models for five dif-ferent 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.
This study set out to explore whether MT systems introduce biases in sentiment expressions. We compared three translation models (fairseq-nllb , Argos-translate , and BERT2BERT ) for five languages (German, English, Hebrew, Spanish, and
Chinese) from the TED2020 and Global Voices corpora. Our statistical analyses (paired t-test and χ2-test) were not able to confirm any bias. The closest to this is the translation from German to English by the Argo translation system, which causes a shift towards neutral sentiments for both corpora. This ‘bias’, however, cannot be substantiated by a notably large WD.