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Document-level neural machine translation (DocNMT) achieves coherenttranslations by incorporating cross-sentence context. However, for mostlanguage pairs there's a shortage of parallel documents, although parallelsentences are readily available. In this paper, we study whether and howcontextual modeling in DocNMT is transferable via multilingual modeling. Wefocus on the scenario of zero-shot transfer from teacher languages withdocument level data to student languages with no documents but sentence leveldata, and for the first time treat document-level translation as a transferlearning problem. Using simple concatenation-based DocNMT, we explore theeffect of 3 factors on the transfer: the number of teacher languages withdocument level data, the balance between document and sentence level data attraining, and the data condition of parallel documents (genuine vs.backtranslated). Our experiments on Europarl-7 and IWSLT-10 show thefeasibility of multilingual transfer for DocNMT, particularly ondocument-specific metrics. We observe that more teacher languages and adequatedata balance both contribute to better transfer quality. Surprisingly, thetransfer is less sensitive to the data condition, where multilingual DocNMTdelivers decent performance with either backtranslated or genuine documentpairs.

Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents