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.

该研究提出了自然语言对话系统中自动推断语义意图和关键词槽位的新方法，通过应用 Apriori 算法，设计了一种包含角色标注，概念挖掘，模式挖掘三步的粗略到精细的三步流程，实验表明该方法不仅适用于领域内和领域间的数据，具有良好的适应性，而且大大减少了手动工作。