Combinatorics

Radiology

Ranking (information retrieval)

Mathematics

Pipeline (software)

Operating system

Data mining

Inverted index

Rank (graph theory)

Language model

Philosophy

Artificial intelligence

Set (abstract data type)

Weighting

Programming language

Natural language processing

Information retrieval

Vocabulary

Computer science

Linguistics

Security token

Search engine indexing

Computer security

Speedup

Medicine

Learning to rank

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT. In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index. Compared to existing methods, DeepImpact improves impact-score modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document. Our experiments show that DeepImpact significantly outperforms prior first-stage retrieval approaches by up to 17% on effectiveness metrics w.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the same effectiveness of state-of-the-art approaches with up to 5.1x speedup in efficiency.

Learning Passage Impacts for Inverted Indexes