Lost in Interpretation:Predicting Untranslated Terminology in Simultaneous Interpretation
Table 1: Translated, non-literally translated, and raw untranslated term annotations obtained in the annotation process using the NAIST STC for (T)ranslator, and {B,A,S}-rank SI. Note that these raw untranslated term figures are directly from the annotation process, prior to filtering based off of the term relevancy constraint from §2.
['[BOLD] T/I', '[BOLD] trans.  [BOLD] #', '[BOLD] trans.  [BOLD] %', '[BOLD] non-lit.  [BOLD] #', '[BOLD] non-lit.  [BOLD] %', '[BOLD] raw untrans.  [BOLD] #', '[BOLD] raw untrans.  [BOLD] %']
Since translators performed in an offline setting without time constraints, they were able to translate the largest number of source terms into the target language, with 80% being literally translated, and 6% being non-literally translated. On the other hand, interpreters tend to leave many source terms uncovered in their translations. The A-rank and B-rank interpreters achieve roughly the same level of term coverage, with the A-rank being only slightly more effective than B-rank at translating terms literally and non-literally. This is in contrast with Shimizu et al. ’s automatic analysis of translation quality on a three-talk subset, in which A-rank has slightly higher translation error rate and lower BLEU score The most experienced S-rank interpreter leaves 17% fewer terms than B-rank uncovered in the translations. More interestingly, the number of non-literally translated terms also correlates with experience-level. In fact, the S-rank interpreter actually exceeds the translator in the number of non-literal translations produced. Non-literal translations can occur when the interpreter fully comprehended the source expression, but chose to generate it in a way that better fit the translation in terms of fluency.