We show that properly training and tuning a relatively simple architecture with a minimal feature set and greedy search achieves state-of-the-art accuracies and beats more complex neural-network architectures.
the model itself is a straightforward stacked bidirectional LSTM (Figure 1) with just two input features at each position (character and bigram).