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Models such as latent semantic analysis and those based on neural embeddingslearn distributed representations of text, and match the query against thedocument in the latent semantic space. In traditional information retrievalmodels, on the other hand, terms have discrete or local representations, andthe relevance of a document is determined by the exact matches of query termsin the body text. We hypothesize that matching with distributed representationscomplements matching with traditional local representations, and that acombination of the two is favorable. We propose a novel document ranking modelcomposed of two separate deep neural networks, one that matches the query andthe document using a local representation, and another that matches the queryand the document using learned distributed representations. The two networksare jointly trained as part of a single neural network. We show that thiscombination or `duet' performs significantly better than either neural networkindividually on a Web page ranking task, and also significantly outperformstraditional baselines and other recently proposed models based on neuralnetworks.

Learning to Match Using Local and Distributed Representations of Text for Web Search