AbstractIntelligent machine translation (MT) is becoming an important field of research and development as the need for translations grows. Currently, the word reordering problem is one of the most important issues of MT systems. To tackle this problem, we present a source-side reordering method using phrasal dependency trees, which depict dependency relations between contiguous non-syntactic phrases. Reordering elements are automatically learned from a reordered phrasal dependency tree bank and are utilized to produce a source reordering lattice. The lattice finally is decoded by a monotone phrase-based SMT to translate a source sentence. The approach is evaluated on syntactically divergent language pairs, i.e. English→Persian and English→German, using the workshop of machine translation 2007 (WMT07) benchmark. The results demonstrate the superiority of the proposed method in terms of translation quality for both translation tasks.