Persian language understanding using a two-step extended hidden vector state parser

The key element of a spoken dialogue system is a spoken language understanding (SLU) unit. Hidden Vector State (HVS) is one of the most popular statistical approaches employed to implement the SLU unit. This paper presents a two-step approach for Persian language understanding. First, a goal detector is used to identify the main goal of the input utterance. Second, after restricting the search space for semantic tagging, an extended hidden vector state (EHVS) parser is used to extract the remaining semantics in each subspace. This will mainly improve the performance of semantic tagger, while reducing the model complexity and training time. Moreover, the need for large amount of data will be reduced importantly due to lowering of data sparseness. Experiments are reported on a Persian corpus, the University Information Kiosk corpus. The experimental results show the effectiveness of the proposed approach compared to HVS and EHVS.