Sequential Labeling with Latent Variables: An Exact Inference Algorithm and its Efficient Approximation

Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming. Furthermore, we describe a straightforward solution on approximating the LDI, and show that the approximated LDI performs as well as the exact LDI, while the speed is much faster. Our experiments demonstrate that the proposed inference algorithm outperforms existing inference methods on a variety of natural language processing tasks.