Task and Workflow Design I

KSE 801Uichin Lee

TurKit: Human Computation Algorithms on Mechanical Turk

Greg Little, Lydia B. Chilton, Rob Miller, and Max Goldman

(MIT CSAIL)UIST 2010

Workflow in M-Turk

HIT

HIT

HIT

HIT

HIT

HIT

Data Collected

in CSV File

Requester posts HIT Groups to

Mechanical Turk

Data Exported for Use

Workflow: Pros & Cons

• Easy to run simple, parallelized tasks.• Not so easy to run tasks in which turkers

improve on or validate each others’ work.

• TurKit to the rescue!

The TurKit Toolkit

• Arrows indicate the flow of information.

• Programmer writes 2 sets of source code:– HTML files for web

servers– JavaScript executed by

TurKit

• Output is retrieved via a JavaScript database.

Turkers

Mechanical Turk

Web Server TurKit

*.html *.js

Programmer

JavaScript Database

Crash-and-rerun programming model

• Observation: local computation is cheap, but the external class cost money

• Managing states over a long running program is challenging– Examples: Computer restarts? Errors?

• Solution: store states in the database (in case)• If an error happens, just crash the program and re-run by

following the history in DB– Throw a “crash” exception; the script is automatically re-run.

• New keyword “once”: – Remove non-determinism– Don’t need to re-execute an expensive operation (when re-run)

• But why should we re-run???

Example: quicksort

Parallelism

• First time the script runs, HITs A and C will be created

• For a given forked branch, if a task fails (e.g., HIT A), TurKit crashes the forked branch (and re-run)

• Synchronization w/ join()

MTurk Functions

• Prompt(message, # of people)– mturk.prompt("What is your favorite color?", 100)

• Voting(message, options)• Sort(message, items)

VOTE() SORT()

TurKit: Implementation

• TurKit: Java using Rhino to interpret JavaScript code, and E4X2 to handle XML results from MTurk

• IDE: Google App Engine3 (GAE)

Online IDE

Exploring Iterative and Parallel Human Computation Processes

Greg Little, Lydia B. ChiltonMax Goldman, Robert C. Miller

HCOMP 2010

HC Task Model

• Dimension: – Dependent (iterative) or independent (parallel) tasks – Creation and decision tasks

• Task model examples

Creation tasks (creating new content): e.g., writing ideas,

imagery solutions, etc.

Decision tasks (voting/rating): e.g., rating the quality of a description of

an image

HC Task Model

• Combining tasks: iterative and parallel tasks

Iterative pattern: a sequence of creation tasks where the result of each task feeds into the next one, followed by a comparison task

Parallel pattern: a set of creation tasks executed in parallel, followed by a task of choosing the best

Experiment: Writing Image Description

• Iterative vs. parallel; each 6 creation tasks ($0.02), followed by rating tasks (1-10 scale, $0.01)

Experiment: Writing Image Description

• Turkers in iterative condition gave better description while parallel condition always shows an empty text area.

Experiment: Writing Image Description

• Average rating after n iterations– After six iterations: 7.9 vs. 7.4, t-test T29=2.1, p=0.04

iterative

parallel

Experiment: Writing Image Description

• Length vs. rating: positive correlation

• The two outliers (circled) represent instances of text copied from the Internet (with superficial description)

Length (characters)

Ratin

g

Experiment: Writing Image Description

• Work Quality:– 31% mainly append content at the end, and make only minor

modifications (if any) to existing content; – 27% modify/expand existing content, but it is evident that they use

the provided description as a basis;– 17% seem to ignore the provided description entirely and start over;– 13% mostly trim or remove content; – 11% make very small changes (adding a word, fixing a misspelling,

etc);– 1% copy-paste superficially related content found on the internet.

• Creating vs. improving (takes about the same time, avg. 211 seconds)

Experiment: Brainstorming

Experiment: Brainstorming

• Iterative work: higher average rating– Biased thinking: e.g., tech -> xxtech -> yytech

• Parallel work: diversity, higher deviation (rating) – No iteration for brainstorming

Iteration Rating

Avg.

Rati

ng

iterative

parallel

Example: Blurry Text Recognition

Example: Blurry Text Recognition

• Iterative performs better than parallel

Iteration

Accu

racy

Summary

• TurKit: a flexible programming tool for m-turk

• Various work-flow can be designed; e.g., iterative, parallel, and hybrid

• Iterative performs better than parallel in several cases (e.g., image description, brainstorming, text recognition)