We present expected F-measure training for shift-reduce parsing with RNNs, which enables the learning of a global parsing model optimized for sentence-level F1. We apply the model to $CCG$ parsing, where it improves over a strong greedy RNN baseline, by 1.47% F1, yielding state-of-the-art results for shift-reduce $CCG$ parsing.