SEINE：面向神经信息检索的基于片段的索引
SEINE: SEgment-based Indexing for NEural information retrieval
AI总结：
提出基于片段的神经索引方法SEINE，通过片段级倒排索引存储查询-文档交互值，灵活支持多种基于交互的神经检索方法，在MQ2007和MQ2008数据集上实现最高28倍加速且不损失检索有效性。
摘要：
许多早期的神经信息检索（NeurIR）方法都是重排序器，由于查询时计算成本高昂，它们依赖于传统的第一阶段检索器。最近，基于表示的检索器受到了广泛关注，它分别学习查询表示和文档表示，使得离线预计算文档表示成为可能，从而减少了查询时的工作量。基于密集和稀疏表示的检索器都已被探索过。然而，这些方法侧重于寻找最能表示文本的表示（即度量学习），而负责查询和文档之间相似度匹配的实际检索函数则通过使用点积保持在最低限度。一个缺点是，与传统的词项级倒排索引不同，由这些嵌入形成的索引不能轻易被另一种检索方法重用。另一个缺点是，将交互保持在最低限度会损害检索有效性。相反，基于交互的检索器以其更好的检索有效性而闻名。在本文中，我们提出了一种新颖的基于片段的神经索引方法SEINE，它提供了一个通用的索引框架，可以灵活地支持多种基于交互的神经检索方法。我们强调对现有神经检索方法中常见组件的仔细分解，并提议使用片段级倒排索引来存储原子级的查询-文档交互值。在LETOR MQ2007和MQ2008数据集上的实验表明，我们的索引方法可以将多种神经检索方法加速多达28倍，而不会牺牲太多有效性。
英文摘要：
Many early neural Information Retrieval (NeurIR) methods are re-rankers that rely on a traditional first-stage retriever due to expensive query time computations. Recently, representation-based retrievers have gained much attention, which learns query representation and document representation separately, making it possible to pre-compute document representations offline and reduce the workload at query time. Both dense and sparse representation-based retrievers have been explored. However, these methods focus on finding the representation that best represents a text (aka metric learning) and the actual retrieval function that is responsible for similarity matching between query and document is kept at a minimum by using dot product. One drawback is that unlike traditional term-level inverted index, the index formed by these embeddings cannot be easily re-used by another retrieval method. Another drawback is that keeping the interaction at minimum hurts retrieval effectiveness. On the contrary, interaction-based retrievers are known for their better retrieval effectiveness. In this paper, we propose a novel SEgment-based Neural Indexing method, SEINE, which provides a general indexing framework that can flexibly support a variety of interaction-based neural retrieval methods. We emphasize on a careful decomposition of common components in existing neural retrieval methods and propose to use segment-level inverted index to store the atomic query-document interaction values. Experiments on LETOR MQ2007 and MQ2008 datasets show that our indexing method can accelerate multiple neural retrieval methods up to 28-times faster without sacrificing much effectiveness.
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。
