Dependency distance (DD) is a syntactic complexity measure proposed by Hudson (). With the development of annotated corpora, researchers have become able to examine the relation between DD and processing difficulty using large-scale data. Based on treebanks of twenty languages, Liu () identified the universal later called “dependency distance minimization”. While the mean DD of languages tends to be minimized as constrained by working memory, there also seem to be considerable differences among languages, particularly between Chinese and English (Hudson, ). To investigate whether this variation is induced by corpus-based factors (such as sentence length, genre and annotation scheme) or by deeper motivations (such as syntactic differences of languages), a series of studies were conducted. Their results indicate that while shorter sentences, informative and spoken texts, as well as annotations based on syntactic functions may contribute to shorter DDs, the DD variation between Chinese and English is more likely the result of different syntactic structures and processing mechanisms (such as the use of words rather than affixes to express tense in Chinese).