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home / papers / 2026 / Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts

Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts

Summary

Machine translation is widely used for political content and for building multilingual datasets. Standard metrics such as BLEU, COMET or CometKiwi reward surface or semantic closeness, so they can miss shifts in the opinion a text conveys. The authors treat stance (favor vs. against a target) as a property that translation should leave unchanged. They test this invariance with two adapted hypothesis tests. McNemar's test compares paired predictions on each source and its translation. A two-proportion Z-test compares translations with native texts carrying the same stance.

Stance is predicted by encoder and decoder classifiers trained on BiMultiSD-XLT, a newly assembled binary stance corpus. A hand-crafted set of French stance-reversed X-Stance items lets the authors inject controlled distortion and confirm that the tests respond to graded manipulation. They then evaluate twelve multilingual models on German, French and English, in both direct and round-trip settings. No system preserves stance in every language direction. This matters for anyone relying on machine-translated benchmarks or deploying MT in civic and news contexts.

Key points

  • Resources released: BiMultiSD-XLT, which merges six existing stance datasets plus their automatic translations into one binary format. Also released: 100 French X-Stance test items whose stance was inverted by native speakers while keeping wording as close as possible. Code is available at github.com/NazaninShafiabadi/mt-invariance.
  • The twelve evaluated systems include the EuroLLM, Llama3.x, Qwen, NLLB and SalamandraTA families. Tests use α = 0.01, and results are stratified by gold label so that opposite flips cannot cancel out.
  • Small models (Llama3.2 1B/3B, NLLB 1.3B/3.3B) show significant stance shifts in almost every condition. EuroLLM-9B and SalamandraTA-7B are the most robust, but they still fail in several directions.
  • Round-trip translation can mislead as a diagnostic. SalamandraTA-7B passes McNemar's test in 3/8 direct-MT scenarios versus 5/8 under RTT, while the Qwen models lose consistency under RTT.
  • Distortion is asymmetric: favor and against labels are altered at different rates within the same language pair.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Machine translation

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2pjio9ho8rxg

@inproceedings{shafiabadi2026biases,
  title = {{Biases in Translation: Assessing Opinion Distortion in Machine Translated Texts}},
  author = {Shafiabadi, Nazanin and Yvon, François},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2pjio9ho8rxg},
  url = {https://doi.org/10.63317/2pjio9ho8rxg}
}

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