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Recently, $k$NN-MT has shown the promising capability of directlyincorporating the pre-trained neural machine translation (NMT) model withdomain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achievedomain adaptation without retraining. Despite being conceptually attractive, itheavily relies on high-quality in-domain parallel corpora, limiting itscapability on unsupervised domain adaptation, where in-domain parallel corporaare scarce or nonexistent. In this paper, we propose a novel framework thatdirectly uses in-domain monolingual sentences in the target language toconstruct an effective datastore for $k$-nearest-neighbor retrieval. To thisend, we first introduce an autoencoder task based on the target language, andthen insert lightweight adapters into the original NMT model to map thetoken-level representation of this task to the ideal representation oftranslation task. Experiments on multi-domain datasets demonstrate that ourproposed approach significantly improves the translation accuracy withtarget-side monolingual data, while achieving comparable performance withback-translation.

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation