Abstract: Acquiring high-quality temporal common sense (TCS) knowledge from free-form text is a crucial but challenging problem for event-centric natural language understanding, due to the language reporting bias problem: people rarely report the obvious events but highlight the special cases. For example, one may rarely report ``I get off the bed in 3 seconds'', but we can observe ``It takes me an hour to get off the bed every morning'' in text. This phenomenon can result in incomplete and biased TCS knowledge. Existing works address this issue mainly by exploiting the interactions among temporal dimensions (e.g., duration, the temporal relation between events) in a multi-task framework. However, this line of work suffers the limitation of implicit, inadequate, and unexplainable interactions. In this paper, we propose a novel Soft Logic Enhanced Event Temporal Reasoning (SLEER) model for acquiring unbiased TCS knowledge, in which the complementary relationship among dimensions are explicitly represented as logic rules and modeled by t-norm fuzzy logics. SLEER can utilize logic rules to regularize its inference process. Experimental results on two intrinsic datasets and two extrinsic datasets show the efficiency of our proposed method.