  Current intent classification approaches assign binary intent class
memberships to natural language utterances while disregarding the inherent
vagueness in language and the corresponding vagueness in intent class
boundaries. In this work, we propose a scheme to address the ambiguity in
single-intent as well as multi-intent natural language utterances by creating
degree memberships over fuzzified intent classes. To our knowledge, this is the
first work to address and quantify the impact of the fuzzy nature of natural
language utterances over intent category memberships. Additionally, our
approach overcomes the sparsity of multi-intent utterance data to train
classification models by using a small database of single intent utterances to
generate class memberships over multi-intent utterances. We evaluate our
approach over two task-oriented dialog datasets, across different fuzzy
membership generation techniques and approximate string similarity measures.
Our results reveal the impact of lexical overlap between utterances of
different intents, and the underlying data distributions, on the fuzzification
of intent memberships. Moreover, we evaluate the accuracy of our approach by
comparing the defuzzified memberships to their binary counterparts, across
different combinations of membership functions and string similarity measures.
