Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation

2025-08-25

AI Summary

Customer service dialogues are hindered by privacy constraints and scarcity of authentic corpora, impeding high-quality synthetic data generation and evaluation. To address their goal-oriented nature, role asymmetry, ASR-induced noise, and regulatory compliance requirements, we propose a multi-stage, feature-aware synthesis method grounded in intent summarization, topic flow modeling, and QA-formatted structuring. Furthermore, we introduce the first multilingual diagnostic framework tailored to customer service scenarios, comprising 18 linguistically and behaviorally grounded metrics. This framework enables fine-grained, reference-free assessment of disfluency, emotional consistency, and agent behavioral realism—marking the first such capability. Empirical analysis reveals systematic biases across existing generation methods: none fully approximate real-world dialogues, with pronounced deficiencies in emotional and behavioral modeling. Our diagnostic suite provides an interpretable, reproducible evaluation pathway for advancing synthetic dialogue systems.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Synthetic transcript generation is critical in contact center domains, where privacy and data scarcity limit model training and evaluation. Unlike prior synthetic dialogue generation work on open-domain or medical dialogues, contact center conversations are goal-oriented, role-asymmetric, and behaviorally complex, featuring disfluencies, ASR noise, and compliance-driven agent actions. In deployments where transcripts are unavailable, standard pipelines still yield derived call attributes such as Intent Summaries, Topic Flow, and QA Evaluation Forms. We leverage these as supervision signals to guide generation. To assess the quality of such outputs, we introduce a diagnostic framework of 18 linguistically and behaviorally grounded metrics for comparing real and synthetic transcripts. We benchmark four language-agnostic generation strategies, from simple prompting to characteristic-aware multi-stage approaches, alongside reference-free baselines. Results reveal persistent challenges: no method excels across all traits, with notable deficits in disfluency, sentiment, and behavioral realism. Our diagnostic tool exposes these gaps, enabling fine-grained evaluation and stress testing of synthetic dialogue across languages.

Problem

• Generating realistic synthetic contact center dialogues for training
• Addressing data scarcity and privacy constraints in dialogue generation
• Evaluating synthetic dialogue quality with linguistically-grounded metrics

Innovation

• Leveraging derived call attributes as supervision signals
• Introducing a diagnostic framework with 18 metrics
• Benchmarking language-agnostic multi-stage generation strategies