Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency. Current learned sparse models learn the weights of terms in documents and, sometimes, queries; however, they exploit different vocabulary structures, document expansion techniques, and query expansion strategies, which can make them slower than traditional sparse models such as BM25. In this work, we propose a novel indexing and query processing technique that exploits a traditional sparse model's "guidance" to efficiently traverse the index, allowing the more effective learned model to execute fewer scoring operations. Our experiments show that our guided processing heuristic is able to boost the efficiency of the underlying learned sparse model by a factor of four without any measurable loss of effectiveness.
翻译:以变压器(如BERT)为基础的神经信息检索结构能够大大提高系统相对于传统稀有模型(如BM25)的系统效力。尽管这些神经方法非常有效,但运行费用非常昂贵,难以在严格的潜伏限制下部署。为解决这一局限性,最近的研究提出了新品种的已知稀有模型,这些模型试图与所学的密集模型的功效相匹配,同时利用传统的反向指数数据结构提高效率。目前所学的稀有模型在文件、有时是查询中学习术语的权重;然而,它们利用不同的词汇结构、文件扩展技术和查询扩展战略,这可以使它们慢于传统的稀疏模型(如BM25),因此,这些神经方法非常昂贵,难以在严格的潜伏限制下部署。在这项工作中,我们建议采用新的索引和查询处理技术,利用传统的稀疏散模型的“指导”有效地绕动指数,使更有效的学习模型能够执行较少的评分操作。我们的实验表明,我们所学的处理超量能够以四个系数提高基本稀少模型的效率,而没有任何可测量的效力。