  Automatically and accurately identifying user intents and filling the
associated slots from their spoken language are critical to the success of
dialogue systems. Traditional methods require manually defining the
DOMAIN-INTENT-SLOT schema and asking many domain experts to annotate the
corresponding utterances, upon which neural models are trained. This procedure
brings the challenges of information sharing hindering, out-of-schema, or data
sparsity in open-domain dialogue systems. To tackle these challenges, we
explore a new task of {\em automatic intent-slot induction} and propose a novel
domain-independent tool. That is, we design a coarse-to-fine three-step
procedure including Role-labeling, Concept-mining, And Pattern-mining (RCAP):
(1) role-labeling: extracting keyphrases from users' utterances and classifying
them into a quadruple of coarsely-defined intent-roles via sequence labeling;
(2) concept-mining: clustering the extracted intent-role mentions and naming
them into abstract fine-grained concepts; (3) pattern-mining: applying the
Apriori algorithm to mine intent-role patterns and automatically inferring the
intent-slot using these coarse-grained intent-role labels and fine-grained
concepts. Empirical evaluations on both real-world in-domain and out-of-domain
datasets show that: (1) our RCAP can generate satisfactory SLU schema and
outperforms the state-of-the-art supervised learning method; (2) our RCAP can
be directly applied to out-of-domain datasets and gain at least 76\%
improvement of F1-score on intent detection and 41\% improvement of F1-score on
slot filling; (3) our RCAP exhibits its power in generic intent-slot
extractions with less manual effort, which opens pathways for schema induction
on new domains and unseen intent-slot discovery for generalizable dialogue
systems.
