Dialog-Based Meaning Derivation Service for Technical Language Domains

In our previous work, we present on how new algorithms can be recognized and learned from human descriptions. In this case, end users are able to extend the given system by their own functionality. During the evaluation, due to language limits on the system, it could interpret only 59 % of user input correctly. In this paper, we provide an approach on the Dialog-based Meaning Derivation Service (DMDS). In case, user input does not match to the system knowledge, DMDS serves various word networks to find relevant synonyms as candidates for the unknown word. DMDS then tries to verify these candidates to the given knowledge base. Finally, matched candidates are presented to the end user by the dialog system for confirmation of a contextual match. The meaning is learned after the user confirmation and is mapped to the given functionality, and can be used afterwards. Therefore, the model developed in this work can be categorized as supervised learning. Finally, both the performance and the quality recorded by input from a user study were examined. However, DMDS improves the correct interpretation of the system from 59 % to 82 %. Our focus is to improve the interaction between humans and machines and enable the end user to instruct programmable devices, without having to learn a programming language.