We experiment graph-based SemiSupervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semisupervised CRF by defining new feature set and altering the label propagation algorithm. Our results demonstrate that our proposed approach significantly improves the performance of the supervised model by utilizing the knowledge gained from the graph.
REF applies graph-based supervised learning of Conditional Random Fields (CRF) for Spoken Language Understanding (SLU) on unaligned data.
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
ArXiv
ArXiv
Computer Science