Hierarchical discriminative model for spoken language understanding

The paper presents a new discriminative model for statistical spoken language understanding designed for use in spoken dialog systems. The parsing algorithm uses lexicalized grammar derived from unaligned training data with probability estimates generated by multiclass classifiers. The generated semantic trees are partially aligned with the input sentence to provide lexical realisation of semantic concepts. The model was evaluated on two semantically annotated corpora and in both tasks it outperforms the baseline Hidden Vector State parser and Semantic Tuple Classifiers model. The experiments were performed using both transcribed data and recognized lattices. The innovative aspect of using phoneme lattices in the understanding process instead of word lattices is examined and described.