Understanding the social roles played by contributors to online communities can facilitate the process of task routing. In this work, we develop new techniques to find roles in Wikipedia based on editors' low-level edit types and investigate how work contributed by people from different roles affect the article quality. To do this, we first built machine-learning models to automatically identify the edit categories associated with edits. We then applied a graphical model analogous to Latent Dirichlet Allocation to uncover the latent roles in editors' edit histories. Applying this technique revealed eight different roles editors play. Finally, we validated how our identified roles collaborate to improve the quality of articles. The results demonstrate that editors carrying on different roles contribute differently in terms of edit categories and articles in different quality stages need different types of editors. Implications for editor role identification and the validation of role contribution are discussed.