Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document reranking
Bibliographic record
Abstract
Abstract The pre‐trained language model (PLM) based on the Transformer encoder, namely BERT, has achieved state‐of‐the‐art results in the field of Information Retrieval. Existing BERT‐based ranking models divide documents into passages and aggregate passage‐level relevance to rank the document list. However, these common score aggregation strategies cannot capture important semantic information such as document structure and have not been extensively studied. In this article, we propose a novel kernel‐based score pooling system to capture document‐level relevance by aggregating passage‐level relevance. In particular, we propose and study several representative kernel pooling functions and several different document ranking strategies based on passage‐level relevance. Our proposed framework KnBERT naturally incorporates kernel functions from the passage level into the BERT‐based re‐ranking method, which provides a promising avenue for building universal retrieval‐then‐rerank information retrieval systems. Experiments conducted on two widely used TREC Robust04 and GOV2 test datasets show that the KnBERT has made significant improvements over other BERT‐based ranking approaches in terms of MAP, P@20, and NDCG@20 indicators with no extra or even less computations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".