This document discusses methods for active content-based task selection in crowdsourcing, focusing on optimizing relevance assessments under budget constraints using information-theoretic approaches. It presents algorithms for variance-based and mutual information-based sampling to improve the efficiency and quality of relevance votes, demonstrating significant budget savings. Experiments conducted on the TREC crowdsourcing dataset illustrate the proposed methods' effectiveness and efficiency, leading to potential applications in other fields.

1. Active Content-Based
Crowdsourcing Task
Selection
Piyush Bansal, Carsten Eickhoff, Thomas Hofmann
ETH Zurich
1

2. Outline
● Past work
○ Exploiting Document content for vote aggregation
● Ongoing extensions
○ Crowdsourcing in extreme budget constraints.
○ Information theoretic approaches
○ Experiments and results
○ Conclusion
2

3. State of the Art
● Crowdsourced relevance assessment cheap and effective
● Quality control via redundancy yields strong performance
● Untapped source of information: document content
● Key idea: Locality of relevance
Davtyan et al. 2015: Exploiting Document Content for Efficient Aggregation of Crowdsourcing Votes
3

4. ●
Clustering Hypothesis for relevance assessment
4

5. Methods
● (informal) Problem statement: Given a set of relevance assessments,
how accurately can we infer the relevance of unjudged Web pages?
○ Solution ideas:
■ Assign same relevance assessment label as nearest neighbor.
■ Borrow relevance assessments from <n> nearest neighbors and
then assign the majority label.
■ Smooth expected relevance across similarity space (KDE, GPs)
○ Baseline:
■ Majority Voting for label aggregation, and coin toss for unjudged
Web pages.
5

6. Davtyan et. al. - Results
6

7. Motivation for our work
Consider the task of search relevance assessment
● Extremely budget-constrained scenario
● Can only ask humans to rate a few Web pages per query
● In previous figure: Number of votes < 1
7

8. A Generic Model of Crowdsourcing
8

9. A Generic Model of Crowdsourcing
9

10. A Generic Model of Crowdsourcing
Difallah et al. 2013: Pick-a-crowd,
Nushi et al. 2015: Crowd Access Path Optimization
10

11. A Generic Model of Crowdsourcing
11

12. A Generic Model of Crowdsourcing
12
Kazai et al. 2011: Worker types and personality traits in crowdsourcing relevance labels
Davtyan et al. 2015: Exploiting Document Content for Efficient Aggregation of Crowdsourcing Votes

13. A Generic Model of Crowdsourcing
13

14. Preliminaries
● RequestVote
○ Sample radom vote from crowd
● AggregateVotes
○ Gaussian Processes (GP) for inferring relevance labels for
unjudged documents.
○ Described by mean function (here: constant),
○ and covariance function (here: linear covariance).
14

15. PickDocument
● What subset of documents to select for labeling?
○ Typical Active learning problem
○ Focus on optimal data acquisition
○ Baseline: Random sampling
● Select points that the classifier is most uncertain about
○ uncertainty based sampling.
15

16. Solution
● Variance-based sampling:
○ Proxy for “uncertainty”, as entropy is a measure of uncertainty
○ Variance-based sampling as approximation to max entropy sampling.
○ In Gaussian processes, the posterior variance does not depend on
the actual observed values of random variables.
16

17. Solution
● Selecting points that maximise variance is NP complete2
● However, this criterion is “submodular"
○ Submodularity (informally): In mathematics, a submodular set function (also
known as a submodular function) is a set function whose value, informally, has the
property that the difference in the incremental value of the function that a single element
makes when added to an input set decreases as the size of the input set increases.
○ However, due to Nemhauser (1978), an approximate solution (1 - 1/e)
OPT to this is achieved via a greedy algorithm.
2 Krause et al. 2008: Near-optimal sensor placements in Gaussian processes
3 Nemhauser et al. 1978: An analysis of approximations for maximizing submodular set functions
17

18. Algorithm: Variance based sampling
18

19. Mutual Information based sampling
● Variance-based sampling is only concerned with reducing
uncertainty at sampled points.
● We care about system-wide uncertainty.
● Maximise Mutual Information b/w selected documents and
rest of space.
● Equivalent to maximally minimising the entropy between
selected documents, and the rest of space (DA).
19

20. Algorithm: MI based sampling
20

21. Experiments
● TREC Crowdsourcing Track 2011 data
● 30 (28) topics
● ~100 documents (ClueWeb’09) to be judged per topic
● ~15 historic votes per query-document pair
● Project documents in 100D doc2vec space
21

22. Results - on TREC2011 CrowdSourcing Dataset.
22

23. Qualitative Analysis
23

24. Conclusions
● Active Learning for Crowdsourcing Vote Sampling
● Two information-theoretic criteria
○ Variance
○ Mutual information
● Saves up to 25% budget at constant quality
● Can be computed efficiently (greedy)
● Does not depend on sampled observations
● In the future: application to other modalities (images, videos)
24

25. Thank you!
Questions?
25