Document-level neural machine translation (DocNMT) achieves coherent
translations by incorporating cross-sentence context. However, for most
language pairs there's a shortage of parallel documents, although parallel
sentences are readily available. In this paper, we study whether and how
contextual modeling in DocNMT is transferable via multilingual modeling. We
focus on the scenario of zero-shot transfer from teacher languages with
document level data to student languages with no documents but sentence level
data, and for the first time treat document-level translation as a transfer
learning problem. Using simple concatenation-based DocNMT, we explore the
effect of 3 factors on the transfer: the number of teacher languages with
document level data, the balance between document and sentence level data at
training, and the data condition of parallel documents (genuine vs.
backtranslated). Our experiments on Europarl-7 and IWSLT-10 show the
feasibility of multilingual transfer for DocNMT, particularly on
document-specific metrics. We observe that more teacher languages and adequate
data balance both contribute to better transfer quality. Surprisingly, the
transfer is less sensitive to the data condition, where multilingual DocNMT
delivers decent performance with either backtranslated or genuine document
pairs.

本文研究了使用多语言模型从有文档的教师语言到只有句子级数据的学生语言的零 - shot 情况下如何将 DocNMT 中的上下文建模转移的问题，首次将文档级翻译视为一种迁移学习问题，并使用基于简单连接的 DocNMT，探索了 3 个因素对转移的影响。实验表明，使用更多的教师语言和适当的数据平衡都有助于提高转移质量。同时多语言 DocNMT 在真实文档对或者反向翻译对数据条件下均可实现较好的性能表现。