Document Expansion by Query Prediction

17 Apr 2019  ·  Rodrigo Nogueira, Wei Yang, Jimmy Lin, Kyunghyun Cho ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Passage Re-Ranking MS MARCO BERT + Doc2query MRR 0.368 # 2
Passage Re-Ranking TREC-PM BERT + Doc2query mAP 36.5 # 1

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