Improving Document Representations By Generating Pseudo Query Embeddings For Dense Retrieval

Abstract

Recently, the retrieval models based on dense representations have been gradually applied in the first stage of the document retrieval tasks, showing better performance than traditional sparse vector space models. To obtain high efficiency, the basic structure of these models is Bi-encoder in most cases. However, this simple structure may cause serious information loss during the encoding of documents since the queries are agnostic. To address this problem, we design a method to mimic the queries on each of the documents by an iterative clustering process and represent the documents by multiple pseudo queries (i.e., the cluster centroids). To boost the retrieval process using approximate nearest neighbor search library, we also optimize the matching function with a two-step score calculation procedure. Experimental results on several popular ranking and QA datasets show that our model can achieve state-of-the-art results.

Stats

• citations17
• S2 citations—
• github stars0
• HF likes0
• heat score9.41
• arxiv keytang2021improving

Related papers

• Learning Diverse Document Representations With Deep Query Interactions For Dense Retrieval (2022)2.51
• Improving Query Representations For Dense Retrieval With Pseudo Relevance Feedback (2021)12.10
• Pseudo-relevance Feedback For Multiple Representation Dense Retrieval (2021)12.93
• Multi-view Document Representation Learning For Open-domain Dense Retrieval (2022)10.21
• Learning Discrete Representations Via Constrained Clustering For Effective And Efficient Dense Retrieval (2021)11.39
• Augmenting Document Representations For Dense Retrieval With Interpolation And Perturbation (2022)5.84
• On The Value Of Behavioral Representations For Dense Retrieval (2022)0.00
• Jointly Optimizing Query Encoder And Product Quantization To Improve Retrieval Performance (2021)12.74