  In this paper we explore the use of meta-knowledge embedded in intent
identifiers to improve intent recognition in conversational systems. As
evidenced by the analysis of thousands of real-world chatbots and in interviews
with professional chatbot curators, developers and domain experts tend to
organize the set of chatbot intents by identifying them using proto-taxonomies,
i.e., meta-knowledge connecting high-level, symbolic concepts shared across
different intents. By using neuro-symbolic algorithms able to incorporate such
proto-taxonomies to expand intent representation, we show that such mined
meta-knowledge can improve accuracy in intent recognition. In a dataset with
intents and example utterances from hundreds of professional chatbots, we saw
improvements of more than 10% in the equal error rate (EER) in almost a third
of the chatbots when we apply those algorithms in comparison to a baseline of
the same algorithms without the meta-knowledge. The meta-knowledge proved to be
even more relevant in detecting out-of-scope utterances, decreasing the false
acceptance rate (FAR) in more than 20\% in about half of the chatbots. The
experiments demonstrate that such symbolic meta-knowledge structures can be
effectively mined and used by neuro-symbolic algorithms, apparently by
incorporating into the learning process higher-level structures of the problem
being solved. Based on these results, we also discuss how the use of mined
meta-knowledge can be an answer for the challenge of knowledge acquisition in
neuro-symbolic algorithms.
