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.

本论文旨在研究神经聊天翻译 (NCT)，针对已有上下文感知机器翻译模型存在的问题，提出了一种多任务多阶段过渡 (MMT) 训练框架，采用双语聊天翻译数据集和额外的单语对话训练 NCT 模型，并引入话语一致性和说话人特征建模。训练过程包括三个阶段，通过逐步转换单语为双语对话，MMT 能够有效地缓解训练阶段的差异并提高翻译效果。