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When Translations Surprise: Human Awareness of Predictability in Translations

Machine translation (MT) has achieved near-human quality for some language pairs, yet its output remains distinct from human translation, primarily in its predictability. While MT systems generate low-perplexity text, humans produce less predictable outputs. This raises the question of whether humans can intuitively use this difference in predictability to distinguish between human- and machine-translated text. We report on a study with 30 native Spanish speakers tasked with identifying the origin of English-to-Spanish translations. We compared their performance against two perplexity-based baselines: a large language model capturing fluency, and a neural MT model, conditioned on the source text, capturing both fluency and adequacy. Our findings reveal that human judgments correlate with fluency-based perplexity, but show no correlation with the perplexity that also accounts for adequacy. This suggests that annotators’ decisions are driven by the target text’s fluency. Consequently, a simple computational baseline using source-aware perplexity significantly outperforms human annotators. This work contributes to a deeper understanding of human perception of MT, highlighting a potential bias in current evaluation protocols toward fluency over adequacy. This bias may lead to an overestimation of the capabilities of highly fluent systems and underscores the need for evaluation methods ensuring translation adequacy is not overlooked.