Hierarchical Modeling for Out-of-Scope Domain and Intent Classification

• URL: http://arxiv.org/abs/2104.14781v1
• Date: Fri, 30 Apr 2021 06:38:23 GMT
• Title: Hierarchical Modeling for Out-of-Scope Domain and Intent Classification
• Authors: Pengfei Liu, Kun Li and Helen Meng
• Abstract summary: This paper focuses on out-of-scope intent classification in dialog systems. We propose a hierarchical multi-task learning approach based on a joint model to classify domain and intent simultaneously. Experiments show that the model outperforms existing methods in terms of accuracy, out-of-scope recall and F1.
• Score: 55.23920796595698
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: User queries for a real-world dialog system may sometimes fall outside the scope of the system's capabilities, but appropriate system responses will enable smooth processing throughout the human-computer interaction. This paper is concerned with the user's intent, and focuses on out-of-scope intent classification in dialog systems. Although user intents are highly correlated with the application domain, few studies have exploited such correlations for intent classification. Rather than developing a two-stage approach that first classifies the domain and then the intent, we propose a hierarchical multi-task learning approach based on a joint model to classify domain and intent simultaneously. Novelties in the proposed approach include: (1) sharing supervised out-of-scope signals in joint modeling of domain and intent classification to replace a two-stage pipeline; and (2) introducing a hierarchical model that learns the intent and domain representations in the higher and lower layers respectively. Experiments show that the model outperforms existing methods in terms of accuracy, out-of-scope recall and F1. Additionally, threshold-based post-processing further improves performance by balancing precision and recall in intent classification.

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