Abstract
Human translators linger on some words and phrases more than others, and predicting this variation is a step towards explaining the underlying cognitive processes. Using data from the CRITT Translation Process Research Database, we evaluate the extent to which surprisal and attentional features derived from a Neural Machine Translation (NMT) model account for reading and production times of human translators. We find that surprisal and attention are complementary predictors of translation difficulty, and that surprisal derived from a NMT model is the single most successful predictor of production duration. Our analyses draw on data from hundreds of translators operating across 13 language pairs, and represent the most comprehensive investigation of human translation difficulty to date.
Abstract (translated)
人类翻译者对一些单词和短语的翻译会持续时间较长，而且预测这种差异是解释潜在认知过程的一个步骤。通过使用来自CRITT翻译过程研究数据库的数据，我们评估了来自神经机器翻译（NMT）模型的超人和注意特征对人类翻译者的阅读和生产时间的解释程度。我们发现，超人和注意力是相互补充的翻译难度的预测因素，而且来自NMT模型的超人是目前最成功的预测生产持续时间的因素。我们的分析基于对13个语言对数百名翻译者的数据，代表了目前对人类翻译难度的最全面的调查。
URL
https://arxiv.org/abs/2312.11852
