Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues
Shilin Qu, Weiqing Wang, Xin Zhou, Haolan Zhan, Zhuang Li, Lizhen Qu,, Linhao Luo, Yuan-Fang Li, and Gholamreza Haffari

TL;DR
This paper presents a scalable method using Large Language Models to construct a comprehensive Chinese Sociocultural NormBase for socially-aware dialogues, leveraging synthetic data and demonstrating its effectiveness in downstream tasks.
Contribution
It introduces a novel approach for building Sociocultural NormBases with LLMs, utilizing synthetic data and contextual frames to improve quality and applicability in dialogue systems.
Findings
Synthetic data yields comparable norm quality to real data.
Frame annotations improve the quality of extracted norms.
SCNs enhance downstream dialogue reasoning tasks.
Abstract
Sociocultural norms serve as guiding principles for personal conduct in social interactions, emphasizing respect, cooperation, and appropriate behavior, which is able to benefit tasks including conversational information retrieval, contextual information retrieval and retrieval-enhanced machine learning. We propose a scalable approach for constructing a Sociocultural Norm (SCN) Base using Large Language Models (LLMs) for socially aware dialogues. We construct a comprehensive and publicly accessible Chinese Sociocultural NormBase. Our approach utilizes socially aware dialogues, enriched with contextual frames, as the primary data source to constrain the generating process and reduce the hallucinations. This enables extracting of high-quality and nuanced natural-language norm statements, leveraging the pragmatic implications of utterances with respect to the situation. As real dialogue…
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Taxonomy
TopicsSpeech and dialogue systems
MethodsBalanced Selection · Attentive Walk-Aggregating Graph Neural Network
