Statement normalization improves enterprise conversation analytics cost efficiency
Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale
Computation and LanguageArtificial IntelligenceMachine Learning
Summary
Analyzing millions of business conversations to answer different questions can be very slow and costly because the same text must be carefully interpreted many times. The authors introduce a way to rewrite conversations into shorter, clearer statements that show who said what, along with helpful labels that highlight important information. This makes it easier and cheaper for computer models to understand and answer questions about the conversations. Their method helps smaller, faster models work together effectively, reducing the computing power needed for large-scale analysis.
What this means in practice
- •For customer service teams: Improve automated detection of offer suppression in call transcripts by transforming conversations into normalized, tagged statements.
- •For enterprise data analysts: Reduce computing costs when analyzing millions of enterprise conversations by sharing normalized representations across multiple questions.
Tested on one dataset.
Authors
Mikhail L. Arbuzov, Karan Dave, Evgeniya Dontsova, Yaodong Hu, Vincent Lao, Navita Jain, Sisong Bei, Dmitry Dimov
Abstract
Enterprise conversation analytics asks many questions of millions of interactions. Each question can require reconstructing what people mean and identifying which information matters, repeating costly interpretive work across the same transcripts. We propose a simple principle: clarify the text, then focus the reader. Statement normalization transforms dialogue into short, speaker-attributed statements with source references and semantic tags. The statements make meaning more explicit; the tags support selecting evidence for a particular question. Downstream models can use the full representation or a relevant subset, depending on what helps them make the decision. In an offer-suppression task on customer-service calls, normalization improves a supervised classifier without selection, while weaker prompted readers benefit from both normalization and selection. A small model can learn the normalization contract, while lightweight encoders handle tagging and downstream decisions. Sharing this preparation across questions supports an inference pipeline built entirely from small models, making analytics over millions of conversations substantially less expensive.