Temporal resolution systems are traditionally tuned to a particular language, requiring significant human effort to translate them to new languages. We present a language independent semantic parser for learning the interpretation of temporal phrases given only a corpus of utterances and the times they reference. We make use of a latent parse that encodes a language-flexible representation of time, and extract rich features over both the parse and associated temporal semantics. The parameters of the model are learned using a weakly supervised bootstrapping approach, without lexical cues or language-specific tuning. We achieve state-of-the-art accuracy on all languages in the TempEval-2 temporal normalization task, reporting a 4%% improvement in both English and Spanish accuracy, and to our knowledge the first results for four other languages.