Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
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Pith papers citing it
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Experiments show domain match and language relatedness drive knowledge transfer in multilingual MT more than vocabulary overlap.
Three small language models vary in retaining fine-grained emotions during backtranslation, with emotion-aware prompting providing improvement and ModernBERT performing similarly to BERT for classification.
citing papers explorer
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Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations
Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
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The Impact of Vocabulary Overlaps on Knowledge Transfer in Multilingual Machine Translation
Experiments show domain match and language relatedness drive knowledge transfer in multilingual MT more than vocabulary overlap.
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Beyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation
Three small language models vary in retaining fine-grained emotions during backtranslation, with emotion-aware prompting providing improvement and ModernBERT performing similarly to BERT for classification.