In this thesis, we examine how such adaptation applies to two important multilingual applications: cross-lingual information retrieval and machine translation. In CLIR, we adapt translation models for domain-specific query translation; in MT, we adapt translation models to heterogeneous target corpora and compare them with previously studied target language model adaptation. We use our adaptation algorithms to enhance state-of-the-art systems, seeking to improve performance under different testing conditions and to reduce the demand for large amounts of domain specific parallel data. We also address the challenge of combining multiple criteria to rank parallel sentence candidates. We investigate Continuous Reactive Tabu Search (CRTS) [2], a global optimization method, as well as Reactive Affine Shaker (RASH) [6], an efficient algorithm which continuously adjusts its search area in order to identify a local minimum. 
