No Query Left Behind: Query Refinement via Backtranslation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Query refinement is to enhance the relevance of search results by modifying users' original queries to refined versions. State-of-the-art query refinement models have been trained on web query logs, which are predisposed to topic drifts. To fill the gap, little work has been proposed to generate benchmark datasets of (query refined query) pairs through an overwhelming application of unsupervised or supervised modifications to the original query while controlling topic drifts. In this paper, however, we propose leveraging natural language backtranslation, a round-trip translation of a query from a source language via target languages, as a simple yet effective unsupervised approach to scale up generating gold-standard benchmark datasets. Backtranslation can (1) uncover terms that are omitted in a query for being commonly understood in a source language, but may not be known in a target language (e.g., figs (tamil) அத்திமரங்கள் the fig trees), (2) augment a query with context-aware synonyms in a target language (e.g., italian nobel prize winners (farsi) برنده های ایتالیایی جایزه نوبل italian nobel laureates), and (3) help with the semantic disambiguation of polysemous terms and collocations (e.g., custer's last stand (malay) pertahan terakhir custer custer's last defence). Our experiments across 5 query sets with different query lengths and topics and 10 languages from 7 language families using 2 neural machine translators validated the effectiveness of query backtranslation in generating a more extensive gold-standard dataset for query refinement. We open-sourced our research at https://github.com/fani-lab/RePair/tree/nqlb.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it