  This paper rethinks translation memory augmented neural machine translation
(TM-augmented NMT) from two perspectives, i.e., a probabilistic view of
retrieval and the variance-bias decomposition principle. The finding
demonstrates that TM-augmented NMT is good at the ability of fitting data
(i.e., lower bias) but is more sensitive to the fluctuations in the training
data (i.e., higher variance), which provides an explanation to a recently
reported contradictory phenomenon on the same translation task: TM-augmented
NMT substantially advances vanilla NMT under the high-resource scenario whereas
it fails under the low-resource scenario. Then we propose a simple yet
effective TM-augmented NMT model to promote the variance and address the
contradictory phenomenon. Extensive experiments show that the proposed
TM-augmented NMT achieves consistent gains over both conventional NMT and
existing TM-augmented NMT under two variance-preferable (low-resource and
plug-and-play) scenarios as well as the high-resource scenario.
