Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
Extrinsic Evaluation of Machine Translation Metrics
2 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.
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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Measuring User's Mental Models of Speech Translation in Human-AI Collaboration
A cross-lingual QA framework shows users build stronger mental models of MT systems through practice and source language knowledge mainly by spotting surface-level errors, with transcriptions helping further.