  Existing neural machine translation (NMT) studies mainly focus on developing
dataset-specific models based on data from different tasks (e.g., document
translation and chat translation). Although the dataset-specific models have
achieved impressive performance, it is cumbersome as each dataset demands a
model to be designed, trained, and stored. In this work, we aim to unify these
translation tasks into a more general setting. Specifically, we propose a
``versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that
works with data from different tasks, and can translate well in multiple
settings simultaneously, and theoretically it can be as many as possible.
Through unified learning, UMLNMT is able to jointly train across multiple
tasks, implementing intelligent on-demand translation. On seven widely-used
translation tasks, including sentence translation, document translation, and
chat translation, our UMLNMT results in substantial improvements over
dataset-specific models with significantly reduced model deployment costs.
Furthermore, UMLNMT can achieve competitive or better performance than
state-of-the-art dataset-specific methods. Human evaluation and in-depth
analysis also demonstrate the superiority of our approach on generating diverse
and high-quality translations. Additionally, we provide a new genre translation
dataset about famous aphorisms with 186k Chinese->English sentence pairs.
