Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning

2022/10/12 by Hui-Chi Kuo, Kuo, Hui-Chi, Yun-Nung Chen +1 · 2 citations
Computer Science · #AI in Service Interactions #Computation and Language (cs.CL) #FOS: Computer and information sciences #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2210.05901

openalex publication_date 2022/10/12 · openalex created_date 2022/10/14 · openalex updated_date 2026/07/28

Abstract

Intelligent virtual assistants are currently designed to perform tasks or services explicitly mentioned by users, so multiple related domains or tasks need to be performed one by one through a long conversation with many explicit intents. Instead, human assistants are capable of reasoning (multiple) implicit intents based on user utterances via commonsense knowledge, reducing complex interactions and improving practicality. Therefore, this paper proposes a framework of multi-domain dialogue systems, which can automatically infer implicit intents based on user utterances and then perform zero-shot prompting using a large pre-trained language model to trigger suitable single task-oriented bots. The proposed framework is demonstrated effective to realize implicit intents and recommend associated bots in a zero-shot manner.

Cited by

• MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning
• IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data

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