SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering

Detractors of neural machine translation admit that while its translations are fluent, it sometimes gets key facts wrong. This is particularly important in simultaneous interpretation where translations have to be provided as fast as possible: before a sentence is complete. Yet, evaluations of simultaneous machine translation (SimulMT) fail to capture if systems correctly translate the most salient elements of a question: people, places, and dates. To address this problem, we introduce a downstream word-by-word question answering evaluation task (SimQA): given a source language question, translate the question word by word into the target language, and answer as soon as possible. SimQA jointly measures whether the SimulMT models translate the question quickly and accurately, and can reveal shortcomings in existing neural systems—hallucinating or omitting facts.