The paper introduces a simple and effective approach to domain adaptation, which is particularly useful when there is sufficient "target" data to improve performance beyond using only "source" data. The method involves augmenting the feature space of both source and target data, making it easy to implement (10 lines of Perl code) and outperforming state-of-the-art techniques on various datasets. The approach is also extendable to multi-domain adaptation problems. The paper formally defines the problem, reviews existing methods, and provides a detailed analysis of the proposed technique, including its kernelized version. Experimental results on multiple sequence labeling tasks demonstrate the effectiveness of the method, showing that it consistently outperforms baseline approaches and other advanced models. The authors also perform model introspection to understand how the learned weights vary across domains, providing insights into the model's behavior. The paper concludes with discussions on future research directions, including theoretical analysis and kernelization interpretations.The paper introduces a simple and effective approach to domain adaptation, which is particularly useful when there is sufficient "target" data to improve performance beyond using only "source" data. The method involves augmenting the feature space of both source and target data, making it easy to implement (10 lines of Perl code) and outperforming state-of-the-art techniques on various datasets. The approach is also extendable to multi-domain adaptation problems. The paper formally defines the problem, reviews existing methods, and provides a detailed analysis of the proposed technique, including its kernelized version. Experimental results on multiple sequence labeling tasks demonstrate the effectiveness of the method, showing that it consistently outperforms baseline approaches and other advanced models. The authors also perform model introspection to understand how the learned weights vary across domains, providing insights into the model's behavior. The paper concludes with discussions on future research directions, including theoretical analysis and kernelization interpretations.