Context Knowledge-aware Recognition of Composite Intents in Task-oriented Human-Bot Conversations - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Context Knowledge-aware Recognition of Composite Intents in Task-oriented Human-Bot Conversations

Abstract

Task-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. While existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge.
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Dates and versions

hal-03693656 , version 1 (13-06-2022)

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Sara Bouguelia, Hayet Brabra, Boualem Benatallah, Marcos Baez, Shayan Zamanirad, et al.. Context Knowledge-aware Recognition of Composite Intents in Task-oriented Human-Bot Conversations. 34th International Conference, CAiSE 2022, Jun 2022, Leuven, Belgium. pp.237-252, ⟨10.1007/978-3-031-07472-1_14⟩. ⟨hal-03693656⟩
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