Multilingual Neural Machine Translation (NMT) excels in sharing knowledge across languages and transferring insights from high-resource languages to improve performance in low-resource languages. However, its performance lags in specific domains such as legal and medical. Previous works have focused on adding language-specific and domain-specific adapters to achieve domain adaptation. Although effective, these adapter-based methods only use domain data to train additional parameters, limiting the performance of multilingual NMT. In this paper, we propose CDSTX, a novel approach that achieves rResearch goal: To what extent does domain adaptation of multilingual models using in-domain data from high-resource languages improve cross-lingual retrieval accuracy on low-resource languages, as measured by precision and recall on the mTREC benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.8/10.