用混合标准增强对话检索数据，提升模型理解用户意图能力。
ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
- 引入大模型生成双向相关性判断，实现可扩展的数据增强
- 在5个基准上优于基线方法，显著提升对话检索效果
- 适合需要提升多轮对话理解的检索系统开发者
对话搜索通过多轮交互满足用户的复杂信息需求，核心挑战在于从上下文依赖的查询中揭示真实搜索意图。以往研究通过微调对话密集检索器，利用查询与文档间的相关性标注进行训练，但该范式面临数据稀缺问题。为此，我们提出ConvMix，一种覆盖更全面的混合标准数据增强框架。通过大语言模型辅助，以可扩展方式设计双向相关性判断增强机制，并集成质量控制策略，生成语义多样且分布贴近的真实样本。在五个常用基准上的实验表明，使用ConvMix训练的对话密集检索器性能优于先前基线方法，验证了其优越有效性。
原文摘要 · Abstract (English)
Conversational search aims to satisfy users' complex information needs via multiple-turn interactions. The key challenge lies in revealing real users' search intent from the context-dependent queries. Previous studies achieve conversational search by fine-tuning a conversational dense retriever with relevance judgments between pairs of context-dependent queries and documents. However, this training paradigm encounters data scarcity issues. To this end, we propose ConvMix, a mixed-criteria framework to augment conversational dense retrieval, which covers more aspects than existing data augmentation frameworks. We design a two-sided relevance judgment augmentation schema in a scalable manner via the aid of large language models. Besides, we integrate the framework with quality control mechanisms to obtain semantically diverse samples and near-distribution supervisions to combine various annotated data. Experimental results on five widely used benchmarks show that the conversational dense retriever trained by our ConvMix framework outperforms previous baseline methods, which demonstrates our superior effectiveness.
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