This work was the first to tackle algorithmic intractability in dealing with complex graphical models. It provided efficient inference through an exact Asymmetric Inside-Outside inference algorithm for hierarchical hidden Markov models. This algorithm was computationally efficient as it scaled linearly in the depth of the hierarchy. This class of inference algorithms also generalises inference in probabilistic context free grammars in natural language processing. This was later extended to derive the first exact and tractable inference algorithm for the hierarchical conditional random fields.