| { |
| "id": "ml23-root", |
| "requirements": "A credible experiment comparing pointwise, pairwise (RankNet-style), and listwise (ListMLE-style) ranking approaches on synthetic query-document datasets: methods are implemented as distinct objectives, execution reports ranking metrics on multiple datasets with multiple seeds, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Well-motivated ranking-objective substitutes (e.g., LambdaRank-style, softrank) that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml23-code", |
| "requirements": "The ranking methods and synthetic grouped datasets are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml23-code-methods", |
| "requirements": "The submission implements multiple distinct conditions \u2014 typically a pointwise baseline plus at least one pairwise and one listwise method \u2014 with genuinely different objective computations, not merely different hyperparameters of one loss.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml23-code-data", |
| "requirements": "A synthetic query-document generator is implemented with grouped queries, per-query document lists, and graded relevance labels, and multiple dataset regimes are instantiated.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml23-code-setup", |
| "requirements": "The experimental setup includes query-level train/test splitting (no leakage of documents from the same query across splits) and a shared scoring-function interface across methods.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml23-exec", |
| "requirements": "Execution logs ranking metrics per method and dataset.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml23-exec-metrics", |
| "requirements": "Execution produces a metrics artifact containing numeric NDCG@k and MAP@k (or equivalents) for each implemented condition on at least one dataset.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml23-exec-seeds", |
| "requirements": "Reported metrics are averaged over multiple random seeds per (condition, dataset) cell with a dispersion measure. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml23-exec-tuning", |
| "requirements": "At least one non-trivial hyperparameter search or documented default is executed (e.g., learning rate/regularization) and the chosen value is logged.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Hyperparameter Tuning" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml23-results", |
| "requirements": "Results analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml23-result-h1", |
| "requirements": "The submission compares the listwise method vs the pointwise baseline in mean NDCG@k per dataset and conveys whether listwise meaningfully improves ranking quality \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml23-result-h2h3", |
| "requirements": "The submission conveys whether at least one ranking-aware method beats the pointwise baseline on NDCG across all datasets (H2) and whether the best ranking-aware method yields a meaningful MAP improvement on most datasets (H3).", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml23-result-writeup", |
| "requirements": "The README or writeup describes methods, datasets, and metric outcomes; conveys per-hypothesis outcomes (supported / refuted / inconclusive); and discusses limitations (synthetic-data realism, scorer capacity, seed count, metric sensitivity). No strict word-count requirement.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
