A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency. On the other hand, quality assessment researchers often suggest that accuracy and fluency are highly correlated and difficult for human raters to distinguish (Callison-Burch et al., 2007). We show that the tension between these views is an instance of Simpson’s paradox, and that accuracy and fluency are positively correlated at the level of the corpus but trade off at the level of individual source segments. We further suggest that the relationship between accuracy and fluency is best evaluated at the segment (or sentence) level, and that the trade off between these dimensions has implications both for assessing translation quality and developing improved MT systems.

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.

Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these behavioral measures are costly and do not scale. In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. We evaluate our approach using behavioural data from translations provided both in and out of context, and report results that improve on a previous embedding-based approach (Thompson et al., 2020). Our work can therefore contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.