Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks

Abstract

Classical information retrieval (IR) methods, such as query likelihood and BM25, score documents independently w.r.t. each query term, and then accumulate the scores. Assuming query term independence allows precomputing term-document scores using these models---which can be combined with specialized data structures, such as inverted index, for efficient retrieval. Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking. We incorporate query term independence assumption into three state-of-the-art neural IR models: BERT, Duet, and CKNRM---and evaluate their performance on a passage ranking task. Surprisingly, we observe no significant loss in result quality for Duet and CKNRM---and a small degradation in the case of BERT. However, by operating on each query term independently, these otherwise computationally intensive models become amenable to offline precomputation---dramatically reducing the cost of query evaluations employing state-of-the-art neural ranking models. This strategy makes it practical to use deep models for retrieval from large collections---and not restrict their usage to late stage re-ranking.

References in corpus (5)

• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• Passage Re-ranking with BERT
• An Updated Duet Model for Passage Re-ranking

Cited by in corpus (11)

• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
• Information Retrieval: Recent Advances and Beyond
• Semantic Models for the First-stage Retrieval: A Comprehensive Review
• Pretrained Transformers for Text Ranking: BERT and Beyond
• Overview of the TREC 2019 deep learning track
• Overview of the TREC 2022 deep learning track
• Efficient Neural Ranking using Forward Indexes
• Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach
• Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
• Duet at TREC 2019 Deep Learning Track
• Non-Linear Multiple Field Interactions Neural Document Ranking