Abstract
Lexicon-based retrieval has gained siginificant popularity in text retrieval due to its efficient and robust performance. To further enhance performance of lexicon-based retrieval, researchers have been diligently incorporating state-of-the-art methodologies like Neural retrieval and text-level contrastive learning approaches. Nonetheless, despite the promising outcomes, current lexicon-based retrieval methods have received limited attention in exploring the potential benefits of feature context representations and term-level knowledge guidance. In this paper, we introduce an innovative method by introducing FEature Context and TErm-level Knowledge modules(FecTek). To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding. Additionally, we develop a term-level knowledge guidance module (TKGM) for effectively utilizing term-level knowledge to intelligently guide the modeling process of term weight. Evaluation of the proposed method on MS Marco benchmark demonstrates its superiority over the previous state-of-the-art approaches.
Abstract (translated)
基于词汇的检索在文本检索中取得了显著的流行， due其高效且鲁棒的性能。为了进一步提高基于词汇的检索的性能，研究人员一直在努力将最先进的方法如神经检索和文本级对比学习方法融入其中。然而，尽管取得了 promising 的结果，现有的基于词汇的检索方法在探索特征上下文表示和词级知识指导的潜在好处方面也受到了限制。在本文中，我们提出了一种创新的方法，即引入了FEature Context和TErm-level Knowledge模块(FecTek)。为了有效地丰富词级权重特征上下文的表示，引入了Feature Context模块(FCM)，它利用了BERT表示的力量来确定每个元素嵌入的动态权重。此外，我们还开发了一个词级知识指导模块(TKGM)，用于有效地利用词级知识指导模型的训练过程。在MS Marco基准上对所提出的方法进行评估，证明了其优越性超过以前的最先进方法。
URL
https://arxiv.org/abs/2404.12152
