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One technique to improve the retrieval effectiveness of a search engine is toexpand documents with terms that are related or representative of thedocuments' content.From the perspective of a question answering system, thismight comprise questions the document can potentially answer. Following thisobservation, we propose a simple method that predicts which queries will beissued for a given document and then expands it with those predictions with avanilla sequence-to-sequence model, trained using datasets consisting of pairsof query and relevant documents. By combining our method with ahighly-effective re-ranking component, we achieve the state of the art in tworetrieval tasks. In a latency-critical regime, retrieval results alone (withoutre-ranking) approach the effectiveness of more computationally expensive neuralre-rankers but are much faster.

Document Expansion by Query Prediction