Automatic Intent-Slot Induction for Dialogue Systems

• URL: http://arxiv.org/abs/2103.08886v1
• Date: Tue, 16 Mar 2021 07:21:31 GMT
• Title: Automatic Intent-Slot Induction for Dialogue Systems
• Authors: Zengfeng Zeng, Dan Ma, Haiqin Yang, Zhen Gou and Jianping Shen
• Abstract summary: We propose 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. We show that our RCAP can generate satisfactory SLU schema and outperforms the state-of-the-art supervised learning method.
• Score: 5.6195418981579435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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