Abstract
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.
Abstract (translated)
现有的神经网络机器翻译(NMT)研究主要关注开发基于不同任务数据的数据集特定模型(例如，文档翻译和聊天翻译)。虽然数据集特定模型已经取得了令人印象深刻的性能，但它们缺点是每个数据集都需要设计、训练和存储一个模型，这变得繁琐。在这项工作中，我们旨在将这些翻译任务统一到一个更一般的背景下。具体来说，我们提出了一个“多功能”模型，即统一模型学习，即NMT统一模型学习，它可以在不同设置下同时翻译，理论上可以翻译尽可能多的数据集。通过统一学习，NMT统一模型能够同时训练多个任务，实现智能按需翻译。在我们最常用的七个翻译任务中，包括句子翻译、文档翻译和聊天翻译，我们的NMT统一模型比数据集特定模型显著提高了性能，并且模型部署成本大大降低。此外，NMT统一模型可以比先进的数据集特定方法实现更具竞争力或更好的性能。人类评估和深入分析也证明了我们的方法在生成多样化高质量的翻译方面的优势。此外，我们提供了关于著名格言的新类型翻译数据集，其中包括186,000个中文到英语句子对。
URL
https://arxiv.org/abs/2305.02777
