An Effective Natural Language Understanding Model using Deep Learning and PyDial Toolkit

In the current world, where we want spoken dialogue systems, to understand natural language utterances by human beings in a very much human way, an effective natural language understanding model plays a crucial role. In the model, we have proposed and used bi-directional LSTM (Long short-term memory network) based Recurrent neural network for slot identification, and we have developed an ontology for a subset of ATIS airlines dataset, describing the slots under different categories (request able, user-request able, in formable) to guide the Spoken dialogue system to help the user finish the task. The novelty in this research comes from the fact that a complete methodology has been proposed to build the NLU model for any domain based on deep learning which can understand utterances in simpler context and understand multi-word slots. With the following methodology, a natural language understanding model can be built for a spoken dialogue system for any domain. In the proposed model, the bi directional lstm model obtained had an accuracy of over 86%. The proposed technique is easy to use and can be helpful in building basic voice based search agents for any domain with good performance.