Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval

2025-10-03

AI Summary

This study addresses the challenge of efficiently retrieving high-value information (e.g., product insights) from massive conversational data. We formally define the novel task of *conversational data retrieval* and identify three core challenges: implicit state recognition, turn-level dynamic modeling, and context-dependent coreference resolution. To advance research, we introduce CDR—the first benchmark for product-oriented conversational retrieval—comprising 1.6K analytical queries and 9.1K dialogue segments spanning five representative analysis tasks. We further propose reusable query templates and a fine-grained error analysis framework. Extensive evaluation across 16 state-of-the-art embedding models reveals a substantial performance gap: the best-performing method achieves only 0.51 NDCG@10, highlighting critical limitations in current approaches. All resources—including the dataset, codebase, and evaluation toolkit—are publicly released to establish a rigorous foundation and catalyze new directions in conversational understanding and retrieval research.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

We present the Conversational Data Retrieval (CDR) benchmark, the first comprehensive test set for evaluating systems that retrieve conversation data for product insights. With 1.6k queries across five analytical tasks and 9.1k conversations, our benchmark provides a reliable standard for measuring conversational data retrieval performance. Our evaluation of 16 popular embedding models shows that even the best models reach only around NDCG@10 of 0.51, revealing a substantial gap between document and conversational data retrieval capabilities. Our work identifies unique challenges in conversational data retrieval (implicit state recognition, turn dynamics, contextual references) while providing practical query templates and detailed error analysis across different task categories. The benchmark dataset and code are available at https://github.com/l-yohai/CDR-Benchmark.

Problem

• Evaluating conversational data retrieval systems for product insights
• Assessing embedding models' performance gap in conversation retrieval
• Addressing unique challenges like implicit state recognition in dialogues

Innovation

• Created conversational data retrieval benchmark for evaluation
• Evaluated 16 embedding models revealing performance gaps
• Identified unique challenges in conversational data retrieval