Simultaneous interpretation is a cognitively taxing task, and even seasoned professionals benefit from real-time assistance. However, both recruiting professional interpreters and evaluating new assistance techniques are difficult. We present a novel, realistic simultaneous interpretation task that mimics the cognitive load of interpretation with crowdworker surrogates. Our task tests different real-time assistance methods in a Wizard-of-Oz experiment with a large pool of proxy users and compares against professional interpreters. Both professional and proxy participants respond similarly to changes in interpreting conditions, including improvement with two assistance interventions—translation of specific terms and of numbers—compared to a no-assistance control.

We examine the inducement of rare but severe errors in English-Chinese and Chinese-English in-domain neural machine translation by minimal deletion of source text with character-based models. By deleting a single character, we can induce severe translation errors. We categorize these errors and compare the results of deleting single characters and single words. We also examine the effect of training data size on the number and types of pathological cases induced by these minimal perturbations, finding significant variation. We find that deleting a word hurts overall translation score more than deleting a character, but certain errors are more likely to occur when deleting characters, with language direction also influencing the effect.