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The semantic matching capabilities of neural information retrieval canameliorate synonymy and polysemy problems of symbolic approaches. However,neural models' dense representations are more suitable for re-ranking, due totheir inefficiency. Sparse representations, either in symbolic or latent form,are more efficient with an inverted index. Taking the merits of the sparse anddense representations, we propose an ultra-high dimensional (UHD)representation scheme equipped with directly controllable sparsity. UHD's largecapacity and minimal noise and interference among the dimensions allow forbinarized representations, which are highly efficient for storage and search.Also proposed is a bucketing method, where the embeddings from multiple layersof BERT are selected/merged to represent diverse linguistic aspects. We testour models with MS MARCO and TREC CAR, showing that our models outperformsother sparse models

Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval