在对话式搜索中将查询表示与改写查询及相关性判断对齐
Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
AI总结：
本文提出QRACDR模型，通过将查询表示与改写查询及相关文档对齐，结合相关性判断训练查询表示模型，在八个数据集上显著提升了对话式搜索和ad-hoc搜索的性能。
摘要：
对话式搜索支持多轮用户与系统交互，以解决复杂的信息需求。与传统的单轮ad-hoc搜索不同，对话式搜索面临着更具挑战性的问题，即在冗长且长尾的对话历史上下文中进行依赖上下文的查询理解。对话式查询改写方法利用显式改写查询来训练改写模型，将依赖上下文的查询转化为独立的搜索查询，但这通常未考虑搜索结果的质量。对话式密集检索方法使用微调来改进预训练的ad-hoc查询编码器，但受到可用于训练的对话式搜索数据的限制。本文利用对话式搜索数据中的改写查询和相关性判断来训练更好的查询表示模型。核心思想是将查询表示与改写查询及相关文档的表示对齐。所提出的模型——查询表示对齐对话式密集检索器（QRACDR），在八个数据集上进行了测试，包括对话式搜索和ad-hoc搜索的各种设置。结果表明，与最先进的方法相比，QRACDR表现出强大的性能，并证实了表示对齐的有效性。
英文摘要：
Conversational search supports multi-turn user-system interactions to solve complex information needs. Different from the traditional single-turn ad-hoc search, conversational search encounters a more challenging problem of context-dependent query understanding with the lengthy and long-tail conversational history context. While conversational query rewriting methods leverage explicit rewritten queries to train a rewriting model to transform the context-dependent query into a stand-stone search query, this is usually done without considering the quality of search results. Conversational dense retrieval methods use fine-tuning to improve a pre-trained ad-hoc query encoder, but they are limited by the conversational search data available for training. In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. The key idea is to align the query representation with those of rewritten queries and relevant documents. The proposed model -- Query Representation Alignment Conversational Dense Retriever, QRACDR, is tested on eight datasets, including various settings in conversational search and ad-hoc search. The results demonstrate the strong performance of QRACDR compared with state-of-the-art methods, and confirm the effectiveness of representation alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
