  One technique to improve the retrieval effectiveness of a search engine is to
expand documents with terms that are related or representative of the
documents' content.From the perspective of a question answering system, this
might comprise questions the document can potentially answer. Following this
observation, we propose a simple method that predicts which queries will be
issued for a given document and then expands it with those predictions with a
vanilla sequence-to-sequence model, trained using datasets consisting of pairs
of query and relevant documents. By combining our method with a
highly-effective re-ranking component, we achieve the state of the art in two
retrieval tasks. In a latency-critical regime, retrieval results alone (without
re-ranking) approach the effectiveness of more computationally expensive neural
re-rankers but are much faster.
