CIRAL: A Test Collection for CLIR Evaluations in African Languages
Bibliographic record
Abstract
Cross-lingual information retrieval (CLIR) continues to be an actively studied topic in information retrieval (IR), and there have been consistent efforts in curating test collections to support its research. However, there is a lack of high-quality human-annotated CLIR resources for African languages: the few existing collections are mostly curated synthetically or from sources with limited corpora for these languages. We present CIRAL, a test collection for cross-lingual retrieval with English queries and passages in four African languages: Hausa, Somali, Swahili, and Yoruba. CIRAL's corpora are obtained from Indigenous African websites and consist of a total of over 2.5 million passages. We gathered over 1,600 queries and 30k high-quality binary relevance judgments annotated by native speakers of the languages. Additional pools were also obtained at CIRAL's shared task, which was hosted at the Forum for Information Retrieval Evaluation 2023 to encourage community participation in CLIR for African languages. We describe the design and curation process of our test collection and provide reproducible baselines that demonstrate CIRAL's utility in evaluating the effectiveness of systems. CIRAL is available at https://github.com/ciralproject/ciral.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".