You can make the model bigger: add more LSTM layers, increase the number of units in the layers, make them bidirectional, add dense layers with activations after the last LSTM or experiment with other architectures.
Other way is to change the number of epochs, batch size and learning rate and see how it affects the results.
If nothing helps, check for class imbalance and how both classes are distributed between the train and validation sets. Apply some basic techniques for imbalances data like using sample weights and generating synthetic data for underrepresented class.
Add more features, if it is possible, or generate new features from existing ones.