Enhancing BERT-based Passage Retriever with Word Recovery Method
Bibliographic record
Abstract
Passage retrieval is a fundamental task in information retrieval research and has received extensive attention on academia and industry in recent years. BERT-based passage retriever adopts a dual-encoder architecture to learn dense representations of queries and passages for semantic matching, which has become an indispensable component of passage retrieval system. However, BERT-based passage retriever tends to ignore phrases and entities mentioned in sentences, which are critical of retrieval. To address this problem, we propose a word recovery method that introduces word-granularity information into BERT-based paragraph retriever. We evaluate our model on three public datasets in different domains including E-commerce, Entertainment Video and Medical. By augmenting BERT-based passage retriever with word recovery method, the retriever achieves consistent improvements in MRR@10 and Reca11@1000, of which MRR@10 increased by 1.2% in Entertainment Video, and Reca11@1000 increased by 0.6% in Ecommerce.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".