Document Expansion by Query Prediction
This addresses the challenge of enhancing document retrieval for search and question answering systems, though it is incremental as it builds on existing sequence-to-sequence models.
The paper tackles the problem of improving search engine retrieval effectiveness by expanding documents with predicted queries, achieving state-of-the-art results in two retrieval tasks and showing that retrieval alone approaches the effectiveness of more expensive neural re-rankers while being faster.
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.