Extending query translation to cross-language query expansion with markov chain models
Why this work is in the frame
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Bibliographic record
Abstract
Dictionary-based approaches to query translation have been widely used in Cross-Language Information Retrieval (CLIR) experiments. However, translation has been not only limited by the coverage of the dictionary, but also affected by translation ambiguities. In this paper we propose a novel method of query translation that combines other types of term relation to complement the dictionary-based translation. This allows extending the literal query translation to related words, which produce a beneficial effect of query expansion in CLIR. In this paper, we model query translation by Markov Chains (MC), where query translation is viewed as a process of expanding query terms to their semantically similar terms in a different language. In MC, terms and their relationships are modeled as a directed graph, and query translation is performed as a random walk in the graph, which propagates probabilities to related terms. This framework allows us to incorporating different types of term relation, either between two languages or within the source or target languages. In addition, the iterative training process of MC allows us to attribute higher probabilities to the target terms more related to the original query, thus offers a solution to the translation ambiguity problem. We evaluated our method on three CLIR benchmark collections, and obtained significant improvements over traditional dictionary-based approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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