  Multi-encoder models are a broad family of context-aware neural machine
translation systems that aim to improve translation quality by encoding
document-level contextual information alongside the current sentence. The
context encoding is undertaken by contextual parameters, trained on
document-level data. In this work, we discuss the difficulty of training these
parameters effectively, due to the sparsity of the words in need of context
(i.e., the training signal), and their relevant context. We propose to
pre-train the contextual parameters over split sentence pairs, which makes an
efficient use of the available data for two reasons. Firstly, it increases the
contextual training signal by breaking intra-sentential syntactic relations,
and thus pushing the model to search the context for disambiguating clues more
frequently. Secondly, it eases the retrieval of relevant context, since context
segments become shorter. We propose four different splitting methods, and
evaluate our approach with BLEU and contrastive test sets. Results show that it
consistently improves learning of contextual parameters, both in low and high
resource settings.
