Cage: A Hybrid Framework for Closed-Domain Conversational Agents

ECML/PKDD (6)(2023)

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Current conversational agents are primarily designed to answer user queries based on structured pre-defined utterance-response pairs. While question-answering (QA) systems extracts potential answers, to queries, from unstructured texts. However, in domain-specific settings, manual creation of query-response pairs is expensive, and domain adaptation of QA platforms is crucial. To this end, we propose Cage, a “hybrid” conversational framework seamlessly integrating structured and unstructured data to obtain precise answers for user queries – improving user experience and quality-of-service. We describe the different components combining query matching and extractive question answering, and demonstrate the multi-lingual chatbot interface provided to a user.
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