Abstract: A ranker for a neural information retrieval model comprises a document encoder having a pretrained language model layer and configured to receive one or more documents and generate a sparse representation for each of the documents predicting term importance of the document over a vocabulary. A separate query encoder is configured to receive a query and generate a representation of the query over the vocabulary. Generated representations are compared to generate a set of respective document scores and rank the one or more documents.

Abstract: Methods and systems for training a dimensionality reduction model. Pairs of proximately located training vectors in a higher dimensional space are generated. Lower dimension vector pairs are generated by encoding first and second training vectors using the dimensionality reduction model, and augmented dimension vector pairs are generated by projecting to an augmented dimensional representation space having a greater number of dimensions. A similarity preservation loss and a redundancy reduction loss are computed and used to optimize parameters of the dimensionality reduction model.

Abstract: Information retrieval methods employ a neural network encoder configured to receive a dense representation and generate a composite code comprising C clusters of dimension L from the dense representation. An activation function is configured to generate a sparse composite code from the composite code. The sparse composite code comprises a binary representation. An index can be generated using the sparse composite code.