Neural Passage Quality Estimation for Static Pruning
<|reference_start|>Neural Passage Quality Estimation for Static Pruning: Neural networks -- especially those that use large, pre-trained language models -- have improved search engines in various ways. Most prominently, they can estimate the relevance of a passage or document to a user's query. In this work, we depart from this direction by exploring whether neural networks can effectively predict which of a document's passages are unlikely to be relevant to any query submitted to the search engine. We refer to this query-agnostic estimation of passage relevance as a passage's quality. We find that our novel methods for estimating passage quality allow passage corpora to be pruned considerably while maintaining statistically equivalent effectiveness; our best methods can consistently prune >25% of passages in a corpora, across various retrieval pipelines. Such substantial pruning reduces the operating costs of neural search engines in terms of computing resources, power usage, and carbon footprint -- both when processing queries (thanks to a smaller index size) and when indexing (lightweight models can prune low-quality passages prior to the costly dense or learned sparse encoding step). This work sets the stage for developing more advanced neural "learning-what-to-index" methods.<|reference_end|>
@article{chang2024neural,
    title={Neural Passage Quality Estimation for Static Pruning},
    author={Xuejun Chang, Debabrata Mishra, Craig Macdonald, Sean MacAvaney},
    journal={arXiv preprint arXiv:2407.12170},
    year={2024},
    doi={10.1145/3626772.3657765},
    archivePrefix={arXiv},
    eprint={2407.12170},
    primaryClass={cs.IR}
}