Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation: Boosting Performance Without Target Domain Labels

This paper explores Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (NMT), aiming to improve translation performance in a target domain by transferring knowledge from a source domain without relying on labeled target data.

Traditional domain adaptation techniques often require labeled data in the target domain, which can be expensive or impractical to obtain in many real-world scenarios. This highlights the importance of unsupervised domain adaptation methods.

The paper introduces a novel non-parametric approach that leverages unlabeled language models and translation models. It utilizes unsupervised adversarial training to adapt the model to the target domain. This is achieved by introducing a shared latent space between the source and target domains. The model is optimized by maximizing the likelihood of source domain sentences in the target domain while minimizing the distribution discrepancy between the two domains through adversarial training.

This method achieves unsupervised domain adaptation in the target domain, leading to significant performance improvements in machine translation tasks. The key advantage is its independence from labeled target domain data, enhancing the model's generalization ability in new domains.

In conclusion, this paper presents a practical and effective non-parametric unsupervised domain adaptation method for NMT. By eliminating the need for target domain labels, the proposed approach offers a promising solution for improving translation quality in low-resource scenarios.