Automated semantic grammar generation in dialogue-based task-oriented systems

Abstract

Dialogue-base task-oriented systems are conversational systems that can complete tasks and answer questions. A semantic grammar can be helpful for user input recognition in such systems. Manually creating semantic grammars can be cumbersome for a developer and delay the deployment of the system, thus it would be of great assistance to automate the process. The topic of automated semantic grammar generation has not been deeply explored and current approaches in this field are either semi-automated or lack interpretability and robustness. In this thesis, the prospect of developing a completely automated semantic grammar generation method was investigated. An additional aim was to create a method that could be employed in different domains without needing any additional modifications. We propose a novel method that utilizes syntactic and relation analysis to infer the semantics of a user input. The method developed is a hybrid approach that comprises statistical methods for the syntactic analysis and a rule-based model for the semantics. The results show that the method is able to generate a fairly accurate and precise semantic grammar. It is also observed that the method is not able to fully analyze all possible pattern cases. Furthermore, the relation analysis has proved to be helpful on finding semantic synonymity amongst different user input patterns. It is plausible to completely automate the creation of a semantic grammar and our findings suggest that hybrid approaches can preform well in this task. Nevertheless, further adjustments should be made in the rule-based model to provide a universal coverage of pattern cases