Papers › Simple Applications of BERT for Ad Hoc Document Retrieval
Simple Applications of BERT for Ad Hoc Document Retrieval
Wei Yang, Haotian Zhang, Jimmy Lin
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval. This required confronting the challenge posed by documents that are typically longer than the length of input BERT was designed to handle. We address this issue by applying inference on sentences individually, and then aggregating sentence scores to produce document scores. Experiments on TREC microblog and newswire test collections show that our approach is simple yet effective, as we report the highest average precision on these datasets by neural approaches that we are aware of.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Ad-Hoc Information Retrieval | TREC Robust04 | BERT FT(Microblog) | MAP | 0.3278 | #17 of 21 | Archive leaderboard | report |
| Ad-Hoc Information Retrieval | TREC Robust04 | BERT FT(Microblog) | P@20 | 0.4287 | #17 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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