Neural machine translation (NMT) models do not work well in domains different from the training data. The standard approach to this problem is to build a small parallel data in the target domain and perform domain adaptation from a source domain where massive parallel data is available. However, domain adaptation between distant domains (e.g., subtitles and research papers) does not perform effectively because of mismatches in vocabulary; it will encounter many domain-specific unknown words (e.g., `angstrom') and words whose meanings shift across domains (e.g., `conductor'). In this study, aiming to solve these vocabulary mismatches in distant domain adaptation, we propose vocabulary adaptation, a simple method for effective fine-tuning that adapts embedding layers in a given pre-trained NMT model to the target domain. Prior to fine-tuning, our method replaces word embeddings in embedding layers of the NMT model, by projecting general word embeddings induced from monolingual data in the target domain onto the source-domain embedding space. Experimental results on distant domain adaptation for English-to-Japanese translation and German-to-English translation indicate that our vocabulary adaptation improves the performance of fine-tuning by 3.6 BLEU points.