Deepct-index could make neural ranking practical "End-to-end ?" it seems that computational expense even when using bert in the reclassification phase Mexico Phone Number List and the latency issues caused is a significant bottleneck for their large-scale use in production environments. Dai highlights the huge benefit of improving the first stage Mexico Phone Number List with deepct, and therefore reducing the burden during the reclassification stage. The main point is that improving the first stage has the potential to significantly improve the first and second stage.
Indeed, a greatly improved first stage could significantly reduce the need for second stages and multiple stages, dai claims, and compares deepct's performance to a standard bm25 first stage filing system. “the high computational cost of deep neuron-based Mexico Phone Number List reclassifications is one of the biggest concerns with their adoption in online services. Nogueira et al. Reported that adding a bert re-ranker, with a re-ranking depth of 1000, introduces 10x more latency in a bm25 first-stage ranking, even when using gpus or tpus. Deepct-index reduces reordering depth from 5× to 10×, making deep neural reorderings practical in latency/resource sensitive systems.
This development is the result of deepct not adding any latency to the search system since nothing more is added. “deepct-index does not add latency Mexico Phone Number List to the search system. The main difference between deepct-index and a typical inverted index is that the term importance weight is based on tfdeepct instead of tf. » (dai, 2020) deepct results dai, emphasizes the rare results obtained using deepct and in particular as an alternative to Mexico Phone Number List forward frequency measurements, which have been used for many years, and argues that the results shown by