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 Dialogbased
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%. ... mehrOur 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.