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Most dominant neural machine translation (NMT) models are restricted to makepredictions only according to the local context of preceding words in aleft-to-right manner. Although many previous studies try to incorporate globalinformation into NMT models, there still exist limitations on how toeffectively exploit bidirectional global context. In this paper, we propose aConfidence Based Bidirectional Global Context Aware (CBBGCA) training frameworkfor NMT, where the NMT model is jointly trained with an auxiliary conditionalmasked language model (CMLM). The training consists of two stages: (1)multi-task joint training; (2) confidence based knowledge distillation. At thefirst stage, by sharing encoder parameters, the NMT model is additionallysupervised by the signal from the CMLM decoder that contains bidirectionalglobal contexts. Moreover, at the second stage, using the CMLM as teacher, wefurther pertinently incorporate bidirectional global context to the NMT modelon its unconfidently-predicted target words via knowledge distillation.Experimental results show that our proposed CBBGCA training frameworksignificantly improves the NMT model by +1.02, +1.30 and +0.57 BLEU scores onthree large-scale translation datasets, namely WMT'14 English-to-German, WMT'19Chinese-to-English and WMT'14 English-to-French, respectively.

Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation