  Simultaneous Machine Translation (SiMT) aims to yield a real-time partial
translation with a monotonically growing the source-side context. However,
there is a counterintuitive phenomenon about the context usage between training
and testing: e.g., the wait-k testing model consistently trained with wait-k is
much worse than that model inconsistently trained with wait-k' (k' is not equal
to k) in terms of translation quality. To this end, we first investigate the
underlying reasons behind this phenomenon and uncover the following two
factors: 1) the limited correlation between translation quality and training
(cross-entropy) loss; 2) exposure bias between training and testing. Based on
both reasons, we then propose an effective training approach called context
consistency training accordingly, which makes consistent the context usage
between training and testing by optimizing translation quality and latency as
bi-objectives and exposing the predictions to the model during the training.
The experiments on three language pairs demonstrate our intuition: our system
encouraging context consistency outperforms that existing systems with context
inconsistency for the first time, with the help of our context consistency
training approach.
