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Although many context-aware neural machine translation models have beenproposed to incorporate contexts in translation, most of those models aretrained end-to-end on parallel documents aligned in sentence-level. Becauseonly a few domains (and language pairs) have such document-level parallel data,we cannot perform accurate context-aware translation in most domains. Wetherefore present a simple method to turn a sentence-level translation modelinto a context-aware model by incorporating a document-level language modelinto the decoder. Our context-aware decoder is built upon only a sentence-levelparallel corpora and monolingual corpora; thus no document-level parallel datais needed. In a theoretical viewpoint, the core part of this work is the novelrepresentation of contextual information using point-wise mutual informationbetween context and the current sentence. We show the effectiveness of ourapproach in three language pairs, English to French, English to Russian, andJapanese to English, by evaluation in \textsc{bleu} and contrastive tests forcontext-aware translation.

Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model