We present a computational model of dialectical argumentation that could serve as a basis for studying elements of legal reasoning. Argumentation is well-suited to decisionmaking in the legal domain, where knowledge is incomplete, uncertain, and inconsistent. We model an argument both as information structure, i.e., argument units connecting claims with supporting data, and as dialectical process, i.e., an alternating series of moves made by opposing sides. Inspired by the legal domain, our model includes burden of proof as a key element, indicating the level of support that must be achieved by a particular side to an argument. Burden of proof acts as a move filter and termination criterion during argumentation and determines the eventual winner. We demonstrate our model by considering two examples that have been discussed previously in the artificial intelligence and legal reasoning literature.