Context Aware Joint Modeling of Domain Classification, Intent Detection and Slot Filling with Zero-Shot Intent Detection Approach

Priya, N. Hema; Tiwari, Abhisek; Saha, Sriparna

doi:10.1007/978-3-030-92238-2_48

Cited by 2 publications

(1 citation statement)

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“…Then recently, the research [ 28 ] proposes a personalized end-to-end task-oriented conversation system that uses a memory network to create attractive and persona-consistent replies. In other recent publications [ 29 – 32 ], the researchers emphasized the DST module to carry out persuasion in task-oriented conversation agents to catch and address dynamic user needs effectively.…”

Section: Related Workmentioning

confidence: 99%

Reinforcing personalized persuasion in task-oriented virtual sales assistant

et al. 2023

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Purpose Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable. Methodology Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasion strategy annotation. Findings The obtained results and detailed analysis firmly establish the effectiveness of the proposed persuasive virtual assistant over traditional task-oriented virtual assistants. The proposed framework considerably increases the quality of dialogue generation in terms of consistency and repetitiveness. Additionally, our experiment with a few shot and zero-shot settings proves that our meta-learned model learns to quickly adopt new domains with a few or even zero no. of training epochs. It outperforms the non-meta-learning-based approaches keeping the base model constant. Originality To the best of our knowledge, this is the first effort to improve a task-oriented virtual agent’s persuasiveness and domain adaptation.

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Section: Related Workmentioning

confidence: 99%