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    <abstract>Recently, kNN-MT (Khandelwal et al., 2020) has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level k-nearest-neighbor (kNN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, it heavily relies on high-quality in-domain parallel corpora, limiting its capability on unsupervised domain adaptation, where in-domain parallel corpora are scarce or nonexistent. In this paper, we propose a novel framework that directly uses in-domain monolingual sentences in the target language to construct an effective datastore for k-nearest-neighbor retrieval. To this end, we first introduce an autoencoder task based on the target language, and then insert lightweight adapters into the original NMT model to map the token-level representation of this task to the ideal representation of the translation task. Experiments on multi-domain datasets demonstrate that our proposed approach significantly improves the translation accuracy with target-side monolingual data, while achieving comparable performance with back-translation. Our implementation is open-sourced at https://github.com/zhengxxn/UDA-KNN.</abstract>
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%0 Conference Proceedings
%T Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
%A Zheng, Xin
%A Zhang, Zhirui
%A Huang, Shujian
%A Chen, Boxing
%A Xie, Jun
%A Luo, Weihua
%A Chen, Jiajun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zheng-etal-2021-non-parametric
%X Recently, kNN-MT (Khandelwal et al., 2020) has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level k-nearest-neighbor (kNN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, it heavily relies on high-quality in-domain parallel corpora, limiting its capability on unsupervised domain adaptation, where in-domain parallel corpora are scarce or nonexistent. In this paper, we propose a novel framework that directly uses in-domain monolingual sentences in the target language to construct an effective datastore for k-nearest-neighbor retrieval. To this end, we first introduce an autoencoder task based on the target language, and then insert lightweight adapters into the original NMT model to map the token-level representation of this task to the ideal representation of the translation task. Experiments on multi-domain datasets demonstrate that our proposed approach significantly improves the translation accuracy with target-side monolingual data, while achieving comparable performance with back-translation. Our implementation is open-sourced at https://github.com/zhengxxn/UDA-KNN.
%R 10.18653/v1/2021.findings-emnlp.358
%U https://aclanthology.org/2021.findings-emnlp.358/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.358
%P 4234-4241

Markdown (Informal)

[Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation](https://aclanthology.org/2021.findings-emnlp.358/) (Zheng et al., Findings 2021)

• Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (Zheng et al., Findings 2021)

ACL

• Xin Zheng, Zhirui Zhang, Shujian Huang, Boxing Chen, Jun Xie, Weihua Luo, and Jiajun Chen. 2021. Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4234–4241, Punta Cana, Dominican Republic. Association for Computational Linguistics.