We introduce a sequence to sequence deep learning algorithm to learn and predict sequences of process alarms and warnings. The proposed recurrent neural network model utilizes an encoder layer of Long Short-Term Memory (LSTM) units to map the input sequence of discrete events into a vector of fixed dimensionality, and a decoder LSTM layer to form a prediction of the sequence of future events. We demonstrate that the information extracted by this model from alarm log databases can be used to suppress alarms with low information content which reduces the operator workload. To generate easily reproducible results and stimulate the development of alarm management algorithms we define an alarm management benchmark problem based on the simulator of a vinyl acetate production technology. The results confirm that sequence to sequence learning is a useful tool in alarm rationalization and, in more general, for process engineers interested in predicting the occurrence of discrete events.