  Neural chat translation (NCT) aims to translate a cross-lingual chat between
speakers of different languages. Existing context-aware NMT models cannot
achieve satisfactory performances due to the following inherent problems: 1)
limited resources of annotated bilingual dialogues; 2) the neglect of modelling
conversational properties; 3) training discrepancy between different stages. To
address these issues, in this paper, we propose a multi-task multi-stage
transitional (MMT) training framework, where an NCT model is trained using the
bilingual chat translation dataset and additional monolingual dialogues. We
elaborately design two auxiliary tasks, namely utterance discrimination and
speaker discrimination, to introduce the modelling of dialogue coherence and
speaker characteristic into the NCT model. The training process consists of
three stages: 1) sentence-level pre-training on large-scale parallel corpus; 2)
intermediate training with auxiliary tasks using additional monolingual
dialogues; 3) context-aware fine-tuning with gradual transition. Particularly,
the second stage serves as an intermediate phase that alleviates the training
discrepancy between the pre-training and fine-tuning stages. Moreover, to make
the stage transition smoother, we train the NCT model using a gradual
transition strategy, i.e., gradually transiting from using monolingual to
bilingual dialogues. Extensive experiments on two language pairs demonstrate
the effectiveness and superiority of our proposed training framework.
