In language translation and intent understanding scenarios, the automated translation of expressions including temporal elements (e.g., calendar dates, date ranges, times, and durations) may be achieved by an implementation of translation techniques, such as compiled rule sets and/or machine learning recognizers that have been trained with a training set. However, sharing development resources among various implementations may be difficult; e.g., updates that extend a rule set for application of the translation techniques to a new context may be difficult to utilize while updating a machine learning recognizer. Presented herein are techniques for facilitating the development of temporal translation resources by providing a temporal translation grammar, comprising recognition rules that specify the recognition of temporal elements; normalization rules that specify the normalization of recognized temporal elements into normalized temporal elements and temporal intent; and translation rules that translate the normalized temporal elements of an expression into dates in a translated expression.