Parser adaptation via Householder transform

We propose a method of adapting a statistical parser using a special orthogonal transform, the Householder transform. Probability mass functions (pmf) in the parser are first mapped to unit sphere, then the Householder transform is applied, which maps a point in unit sphere to another point in unit sphere. The final model is obtained by mapping the transformed point in unit sphere back to simplex through a square map. The proposed method is tested on a semantic parser, and over 20% relative reduction of parse errors can be achieved.