Online Decoding of Markov Models Under Latency Constraints

Abstract

The Viterbi algorithm is an efficient and optimal method for decoding linear-chain Markov Models. However, the entire input sequence must be observed before the labels for any time step can be generated, and therefore Viterbi cannot be directly applied to online/interactive/streaming scenarios without incurring significant (possibly unbounded) latency. A widely used approach is to break the input stream into fixed-size windows, and apply Viterbi to each window. Larger windows lead to higher accuracy, but result in higher latency. We propose several alternative algorithms to the fixed-sized window decoding approach. These approaches compute a certainty measure on predicted labels that allows us to trade off latency for expected accuracy dynamically, without having to choose a fixed window size up front. Not surprisingly, this more principled approach gives us a substantial improvement over choosing a fixed window. We show the effectiveness of the approach for the task of spotting semi-structured information in large documents. When compared to full Viterbi, the approach suffers a 0.1 percent error degradation with a average latency of 2.6 time steps (versus the potentially infinite latency of Viterbi). When compared to fixed windows Viterbi, we achieve a 40x reduction in error and 6x reduction in latency.