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.

翻译：对话式搜索旨在通过多轮交互满足用户复杂的信息需求，其核心挑战在于从上下文相关的查询中揭示真实用户的搜索意图。以往研究通过微调对话式密集检索器，利用上下文相关查询与文档间的相关性判断来实现对话式搜索。然而，该训练范式面临数据稀缺问题。为此，我们提出ConvMix——一种混合标准框架以增强对话式密集检索，其覆盖维度超越现有数据增强框架。我们借助大语言模型，以可扩展方式设计了双向相关性判断增强机制。此外，该框架融合了质量控制机制以获取语义多样的样本，并通过近分布监督整合多种标注数据。在五个广泛使用的基准测试上的实验结果表明，采用ConvMix框架训练的对话式密集检索器优于现有基线方法，验证了本框架的卓越有效性。