mlsanityData to 7 Oct 2026

Measuring Sentiment Bias in Machine Translation

arXiv 2306.07152 ↗First posted 12 Jun 2023cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

36 of 51 references.

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

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  5. 2025Semantic Label Drift in Cross-Cultural Translationcited by 2