Challenge: Multi-way parallel, machine generated content dominates the translations in lower resource languages . a limited investigation suggests this selection bias is the result of low quality content generated in English and translated into many lower resource language via MT.
Approach: They show that multi-way parallel, machine generated content dominates translations in many languages . they also find evidence of a selection bias in the type of content which is translated into many languages.
Outcome: The results suggest that the low quality of multi-way translations on the web was likely created using machine translation.

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Challenge: In this study, we explore massively multilingual low-resource neural machine translation.
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Challenge: Commercial translation systems support only one hundred languages or fewer . commercial translation systems do not make these models available for transfer to low resource languages .
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Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark (2025.emnlp-main)

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Challenge: Multilingual machine translation (MT) benchmarks are widely used to evaluate the capabilities of modern MT systems.
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Challenge: Using machine translation tools for everyday tasks is becoming more commonplace, but a lack of evaluation strategies and alternatives can cause users to over-rely on it.
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Challenge: Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data.
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Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (P19-1)

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Challenge: Using a dictionary, given a rough, target language natives can uncover the latent, fully-fluent rendering of the translation.
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Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)

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Challenge: Existing studies have shown that existing models amplify biases observed in training data.
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Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens (2026.findings-acl)

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Challenge: afan oromo, amharic, and tigrinya are low-resourced languages . they are used for training, benchmarks, news, health, and sports . afono o'mara: quantity does not guarantee quality of MT datasets .
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