Optimized Judge Assignment under Constraints

• US 20130152091A1
• Filed: 12/08/2011
• Published: 06/13/2013
• Est. Priority Date: 12/08/2011
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Liu, Chao, Shepherd, Frederick Bruce
• Application Number:
  US13/314,676
• Publication Number:
  US 20130152091A1
• Time in Patent Office: Days
• US Class Current: 718/102
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Described is a technology by which an assignment model is computed to distribute labeling tasks among judging entities (judges). The assignment model is optimized by obtaining accuracy-related data of the judges, e.g., by probing the judges with labeling tasks having a gold standard label and evaluating the judges'"'"' labels against the gold standard labels, and optimizing for accuracy. Optimization may be based upon on or more other constraints, such as per-judge cost and/or quota.

Claims

1. In a computing environment, a method performed at least in part on at least one processor comprising, providing probing tasks to judges to obtain accuracy-related data with respect to labeling tasks, and using the accuracy-related data to compute an assignment model for assigning other labeling tasks to at least some of the judges, in which the assignment model distributes the other labeling tasks based at least in part upon the accuracy-related data.

2. The method of claim 1 wherein using the accuracy-related data comprises performing an optimization.

3. The method of claim 1 wherein using the accuracy data comprises performing a minimization based upon error data corresponding to the accuracy-related data.

4. The method of claim 1 wherein using the accuracy-related data comprises performing an optimization based upon the accuracy data and cost data.

5. The method of claim 1 wherein using the accuracy-related data comprises performing an optimization based upon the accuracy data and quota data.

6. The method of claim 1 wherein using the accuracy-related data comprises performing an optimization based upon the accuracy data, cost data and quota data.

7. The method of claim 1 wherein providing the probing tasks to judges comprises inserting the probing tasks among regular labeling tasks assigned to the judges.

8. The method of claim 1 wherein providing probing tasks to judges to obtain the accuracy-related data comprises evaluating probing labels received from the judges that correspond to the probing tasks against gold standard labels that correspond to the probing tasks.

9. A system comprising, an assignment engine configured to provide regular tasks and probing tasks to a plurality of judges for labeling, and an optimization mechanism configured to evaluate probed labels corresponding to the probing tasks against gold standard labels to obtain accuracy-related data and optimize an assignment model based at least in part upon the accuracy-related data, in which the assignment model is useable to distribute other labeling tasks based at least in part upon the accuracy-related data to at least some of the judges.

10. The system of claim 9 wherein the optimization mechanism optimizes the assignment model based upon the accuracy-related data and at least one constraint.

11. The system of claim 9 wherein the at least one constraint comprises a quota associated with each judge.

12. The system of claim 9 wherein the at least one constraint comprises a cost associated with each judge.

13. The system of claim 9 wherein the at least one constraint comprises a quota associated with each judge.

14. The system of claim 9 wherein the at least one constraint comprises a cost associated with each judge and a quota associated with each judge.

15. The system of claim 9 wherein optimization mechanism performs an l_p norm minimization.

16. The system of claim 9 wherein the regular tasks and probing tasks correspond to labeling query, URL pairs with a relevance score.

17. One or more computer-readable media having computer-executable instructions, which when executed perform steps, comprising:

receiving a task set comprising labeling tasks for assigning to judges; and

distributing the labeling tasks from the task set according to an assignment model, in which the assignment model is optimized at least in part according to per-judge accuracy-related data obtained by probing of the judges.

18. The one or more computer-readable media of claim 17 having further computer-executable instructions comprising, optimizing the assignment model based at least in part upon the per-judge accuracy-related data and per-judge cost data.

19. The one or more computer-readable media of claim 17 having further computer-executable instructions comprising, optimizing the assignment model based at least in part upon the per-judge accuracy-related data and per-judge quota data.

20. The one or more computer-readable media of claim 17 having further computer-executable instructions comprising, receiving labels from the judges, and using the labels in machine learning.