  Neural Chat Translation (NCT) aims to translate conversational text into
different languages. Existing methods mainly focus on modeling the bilingual
dialogue characteristics (e.g., coherence) to improve chat translation via
multi-task learning on small-scale chat translation data. Although the NCT
models have achieved impressive success, it is still far from satisfactory due
to insufficient chat translation data and simple joint training manners. To
address the above issues, we propose a scheduled multi-task learning framework
for NCT. Specifically, we devise a three-stage training framework to
incorporate the large-scale in-domain chat translation data into training by
adding a second pre-training stage between the original pre-training and
fine-tuning stages. Further, we investigate where and how to schedule the
dialogue-related auxiliary tasks in multiple training stages to effectively
enhance the main chat translation task. Extensive experiments in four language
directions (English-Chinese and English-German) verify the effectiveness and
superiority of the proposed approach. Additionally, we have made the
large-scale in-domain paired bilingual dialogue dataset publicly available to
the research community.
