  As deep neural networks continue to revolutionize various application
domains, there is increasing interest in making these powerful models more
understandable and interpretable, and narrowing down the causes of good and bad
predictions. We focus on recurrent neural networks, state of the art models in
speech recognition and translation. Our approach to increasing interpretability
is by combining a long short-term memory (LSTM) model with a hidden Markov
model (HMM), a simpler and more transparent model. We add the HMM state
probabilities to the output layer of the LSTM, and then train the HMM and LSTM
either sequentially or jointly. The LSTM can make use of the information from
the HMM, and fill in the gaps when the HMM is not performing well. A small
hybrid model usually performs better than a standalone LSTM of the same size,
especially on smaller data sets. We test the algorithms on text data and
medical time series data, and find that the LSTM and HMM learn complementary
information about the features in the text.
