Diversified hidden Markov models for sequential labeling

Labeling of sequential data is a prevalent metaproblem in a wide range of real world applications. A first-order hidden Markov model (HMM) provides a fundamental approach for sequential labeling. However, it does not show satisfactory performance for real world problems, such as optical character recognition (OCR). Aiming at addressing this problem, important extensions of HMM have been proposed in literature. One of the common key features in these extensions is the incorporation of proper prior information. In this paper, we propose a new extension of HMM, termed diversified hidden Markov models (dHMM), with incorporating a diversity-encouraging prior. The prior is added over the state-transition probabilities and thus facilitates more dynamic sequential labelling. Specifically, the diversity is modeled with a continuous determinantal point process. An EM framework for parameter learning and MAP inference is derived, and empirical evaluation on OCR dataset verifies its effectiveness.