Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models

TL;DR

This paper proposes a hybrid approach combining LSTM neural networks with Hidden Markov Models to improve interpretability and performance in speech recognition and translation tasks.

Contribution

It introduces a method to integrate HMMs with LSTMs, enhancing interpretability and often outperforming standalone LSTMs, especially on smaller datasets.

Findings

Hybrid models outperform standalone LSTMs on small datasets.

LSTM and HMM learn complementary information.

The approach improves interpretability of neural networks.

Abstract

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.