Document Expansion by Query Prediction

Code

• github.com/nyu-dl/dl4ir-doc2query
  OfficialIn papertf★ 0
• github.com/castorini/Anserini
  none★ 1,108
• github.com/irgroup/clef2023-longeval-irc
  pytorch★ 1
• github.com/castorini/docTTTTTquery
  pytorch★ 0
• github.com/kasys-lab/anserini-kasys
  none★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions