the university carlos iii of madrid at trec 2011 crowdsourcing track: notebook paper

TREC 2011 Gaithersburg, USA · November 18th Picture by Michael Dornbierer

Practical and Effective Design of a Crowdsourcing Task for

Unconventional Relevance Judging Julián Urbano @julian_urbano

Mónica Marrero, Diego Martín, Jorge Morato, Karina Robles and Juan Lloréns University Carlos III of Madrid

Task I

Crowdsourcing Individual Relevance Judgments

In a Nutshell

• Amazon Mechanical Turk, External HITs

• All 5 documents per set in a sigle HIT = 435 HITs

• $0.20 per HIT = $0.04 per document

graded slider hterms

Hours to complete 8.5 38 20.5

HITs submitted (overhead) 438 (+1%) 535 (+23%) 448 (+3%)

Submitted workers (just preview) 29 (102) 83 (383) 30 (163)

Average documents per worker 76 32 75

Total cost (including fees) $95.7 $95.7 $95.7

ran out of time

Document Preprocessing

• Ensure smooth loading and safe rendering

– Null hyperlinks

– Embed all external resources

– Remove CSS unrelated to style or layout

– Remove unsafe HTML elements

– Remove irrelevant HTML attributes

4

Display Mode

• With images

• Black & white, no images

5

hterms run

Display Mode (and II)

• Previous experiment

– Workers seem to prefer images and colors

– But some definitelly go for just text

• Allow them both, but images by default

• Black and white best with highlighting

– 7 (24%) workers in graded

– 21 (25%) in slider

– 12 (40%) in hterms 6

HIT Design

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Relevance Question

• graded: focus on binary labels

• Binary label

– Bad = 0, Good = 1

– Fair: different probabilities? Chose 1 too

• Ranking

– Order by relevance, then by failures in Quality Control and then by time spent

Relevance Question (II)

• slider: focus on ranking

• Do not show handle at the beginning

– Bias

– Lazy indistinguishable from undecided

• Seemed unclear it was a slider

Relevance Question (III)

10 slider value

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quency

0 20 40 60 80 100

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Relevance Question (IV)

• Binary label

– Threshold

– Normalized between 0 and 100

– Worker-Normalized

– Set-Normalized

– Set-Normalized Threshold

– Cluster

• Ranking label

– Implicit

threshold = 0.4

Relevance Question (and V)

• hterms: focus on ranking, seriously

• Still unclear?

slider value

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Quality Control

• Worker Level: demographic filters

• Task Level: additional info/questions

– Implicit: work time, behavioral patterns

– Explicit: additional verifiable questions

• Process Level: trap questions, training

• Aggregation Level: consensus from redundancy

QC: Worker Level

• At least 100 total approved HITs

• At least 95% approved HITs

– 98% in hterm

• Work in 50 HITs at most

• Also tried

– Country

– Master Qualifications

QC: Implicit Task Level

• Time spent in each document

– Images and Text modes together

• Don’t use time reported by Amazon

– Preview + Work time

• Time failure: less than 4.5 secs

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QC: Implicit Task Level (and II)

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Time Spent (secs)

graded slider hterms

Min 3 3 3

1st Q 10 14 11

Median 15 23 19

QC: Explicit Task Level

• There is previous work with Wikipedia

– Number of images

– Headings

– References

– Paragraphs

• With music / video

– Aproximate song duration

• Impractical with arbitrary Web documents

QC: Explicit Task Level (II)

• Ideas

– Spot nonsensical but syntactically correct sentences

“the car bought a computer about eating the sea”

• Not easy to find the right spot to insert it

• Too annoying for clearly (non)relevant documents

– Report what paragraph made them decide

• Kinda useless without redundancy

• Might be several answers

• Reading comprehension test

QC: Explicit Task Level (III)

• Previous experiment

– Give us 5-10 keywords to describe the document

• 4 AMT runs with different demographics

• 4 faculty members

– Nearly always gave the top 1-2 most frequent terms

• Stemming and removing stop words

• Offered two sets of 5 keywords, choose the one better describing the document

19

QC: Explicit Task Level (and IV)

• Correct

– 3 most frequent + 2 in the next 5

• Incorrect

– 5 in the 25 least frequent

• Shuffle and random picks

• Keyword failure: chose the incorrect terms

20

QC: Process Level

• Previous NIST judgments as trap questions?

• No

– Need previous judgments

– Not expected to be balanced

– Overhead cost

– More complex procress

– Do not tell anything about non-trap examples

21

Reject Work and Block Workers

• Limit the number of failures in QC

22

Action Failure graded slider hterms

Reject HIT Keyword 1 0 1

Time 2 1 1

Block Worker Keyword 1 1 1

Time 2 1 1

Total HITs rejected 3 (1%) 100 (23%) 13 (3%)

Total Workers blocked 0 (0%) 40 (48%) 4 (13%)

Workers by Country Preview Accept Reject % P % A % R Preview Accept Reject % P % A % R Preview Accept Reject % P % A % R

Australia 8 100% Bangladesh 15 3 2 75% 60% 40% Belgium 2 100% Canada 2 100% 1 100% 1 100% Croatia 4 1 80% 100% 0% Egypt 1 100% Finland 11 50 1 18% 98% 2% 4 100% 8 43 1 15% 98% 2% France 9 24 2 26% 92% 8% Germany 1 100% Guatemala 1 100% India 236 214 1 52% 100% 0% 543 235 63 65% 79% 21% 235 190 7 54% 96% 4% Indonesia 2 100% Jamaica 8 5 62% 100% 0% Japan 6 100% 2 100% Kenya 2 100% Lebanon 3 100% Lithuania 6 1 86% 100% 0% Macedonia 1 100% Moldova 1 100% Netherlands 1 100% Pakistan 1 100% 4 1 80% 100% 0% 1 100% Philippines 8 100% 3 100% Poland 1 100% Portugal 2 100% Romania 1 100% 1 100% 5 100% Saudi Arabia 3 1 75% 100% 0% 4 4 50% 100% 0% Slovenia 2 1 67% 100% 0% 3 1 75% 100% 0% 8 2 80% 100% 0% Spain 16 100% 15 100% 9 100% Switzerland 1 100% 7 100% United Arab Emirates 27 12 1 68% 92% 8% 35 9 80% 100% 0% United Kingdom 8 3 73% 100% 0% 18 18 2 47% 90% 10% 8 16 33% 100% 0% United States 246 166 1 60% 99% 1% 381 110 28 73% 80% 20% 242 174 5 57% 97% 3% Yugoslavia 17 14 2 52% 88% 13% 1 100%

Average 527 435 3 77% 99% 1% 1086 428 100 83% 90% 10% 579 435 13 85% 99% 1%

Results (unofficial) Consensus Truth Acc Rec Prec Spec AP NDCG

Median .761 .752 .789 .700 .798 .831 graded .667 .702 .742 .651 .731 .785 slider .659 .678 .710 .632 .778 .819

hterms .725 .725 .781 .726 .818 .846

NIST Truth Acc Rec Prec Spec AP NDCG

Median .623 .729 .773 .536 .931 .922 graded .748 .802 .841 .632 .922 .958 slider .690 .720 .821 .607 .889 .935

hterms .731 .737 .857 .728 .894 .932

Task II

Aggregating Multiple Relevance Judgments

Good and Bad Workers

• Bad ones in politics might still be good in sports

• Topic categories to distinguish

– Type: Closed, limited, navigational, open-ended, etc.

– Subject: politics, people, shopping, etc.

– Rareness: topic keywords in Wordnet?

– Readability: Flesch test

GetAnotherLabel

• Input

– Some known labels

– Worker responses

• Output

– Expected label of unknowns

– Expected quality for each worker

– Confusion matrix for each worker

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Step-Wise GetAnotherLabel

• For each worker wi compute expected quality qi on all topics and quality qij on each topic type tj.

• For topics in tj, use only workers with qij>qi

• We didn’t use all known labels by good workers to compute their expected quality (and final label), but only labels in the topic category

• Rareness seemed to work slightly better

Train Rule and SVM Models

• Relevant-to-nonrelevant ratio

– Unbiased majority voting

• For all workers , average correct-to-incorrect ratio when saying relevant/nonrelevant

• For all workers, average posterior probability of relevant/nonrelevant

– Based on the confusion matrix from GerAnotherLabel

Results (unofficial) Consensus Truth Acc Rec Prec Spec AP NDCG

Median .811 .818 .830 .716 .806 .921 rule .854 .818 .943 .915 .791 .904 svm .847 .798 .953 .931 .855 .958

wordnet .630 .663 .730 .574 .698 .823

NIST Truth Acc Rec Prec Spec AP NDCG

Median .640 .754 .625 .560 .111 .359 rule .699 .754 .679 .644 .166 .415 svm .714 .750 .700 .678 .082 .331

wordnet .571 .659 .560 .484 .060 .299

Sum Up

• Really work the task design

– “Make it simple, but not simpler” (A. Einstein)

– Make sure they understand it before scaling up

• Find good QC methods at the explicit task level for arbitrary Web pages

– Was our question too obvious?

• Pretty decent judgments compared to NIST’s

• Look at the whole picture: system rankings

• Study long-term reliability of Crowdsourcing

– You can’t prove God doesn’t exist

– You can’t prove Crowdsourcing works