Wednesday, April 29, 2015

Title:Incentives in Online Contribution: A game-theoretic framework
Speaker: Arpita Ghosh (School of Computing and Information Science, Cornell University)
Date: Tuesday, May 5th
Time: 12-1pm
Room: NSH 3305

User contribution---whether explicit, as in online crowdsourcing and user-generated content systems, or implicit, via utilization of user data---is central to the Web. The efficacy of systems based on user contribution, however, hinges on users actually participating and behaving as intended by the designer. How does one design various aspects of online contribution platforms---algorithms, reward allocation mechanisms, interfaces---to elicit `good’ outcomes, given that the potential participants in these systems are economic agents with their own costs and benefits to contribution? In this talk, we describe a game-theoretic framework for incentive design for online contribution. To illustrate this framework, we investigate widely-used reward mechanisms in user-generated content and crowdsourcing platforms on the Web---such as badges and leaderboards---in the context of equilibrium outcomes that arise when potential users derive benefit from these social-psychological rewards but incur a cost to contribution. Motivated by a growing literature suggesting that user behavior in online environments might deviate from standard economic models, we explore the idea of `behavioral’ design---theoretical analysis with abstract models based on `real’ behavior---in the context of two problems: the optimal design of contests, widely used in crowdsourcing and user-generated content platforms, and the optimal structure of contracts for crowdwork. Our analysis of equilibrium outcomes in these environments translates to design guidelines in the presence of strategic behavior, and illustrates the idea that formal analysis can inform wide-ranging aspects of the design of online environments for user contribution.

Arpita Ghosh is an Associate Professor of Information Science in the School of Computing and Information Science at Cornell University. She received her B.Tech from IIT Bombay in 2001, and her PhD from Stanford in 2006. Prior to joining Cornell, she spent 6 years (2006-2012) in the Microeconomics and Social Sciences group at Yahoo! Research.

Monday, April 6, 2015

Title:Crowdsourcing Translation
Speaker: Chris Callison Burch (Computer and Information Science Department, University of Pennsylvania)
Date: Tuesday, April 7th
Time: 1:30-2:30 pm
Room: GHC 6501

Modern approaches to machine translation are data-driven. Statistical translation models are trained using parallel text, which consist of sentences in one language paired with their translation into another language. One advantage of statistical translation models is that they are language independent, meaning that they can be applied to any language that we have training data for. Unfortunately, most of the world's languages do not have sufficient amounts of training data to achieve reasonable translation quality. In this talk, I will detail my experiments using Amazon Mechanical Turk to create crowd-sourced translations for "low resource" languages that we do not have training data for. I will discuss the following topics: * Quality control: Can non-expert translators produce translations approaching the level of professional translators? * Cost: How much do crowdsourced translations cost compared to professional translations? * Impact of quality on training: When training a statistical model, What is the appropriate trade-off between small amounts of high quality data v. larger amounts of lower quality data? * Languages: Which low resource languages is it possible to translate on Mechanical Turk? What volumes of data can we collect, and how fast? * Implications: What implications does this have for national defense, disaster response, computational linguistics research, and companies like Google?

Chris Callison-Burch is the Aravind K Joshi term assistant professor in the Computer and Information Science Department at the University of Pennsylvania. Before joining Penn, he was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University for 6 years. He was the Chair of the Executive Board of the North American chapter of the Association for Computational Linguistics (NAACL) from 2011-2013, and he has served on the editorial boards of the journals Transactions of the ACL (TACL) and Computational Linguistics. He has more than 80 publications, which have been cited more than 5000 times. He is a Sloan Research Fellow, and he has received faculty research awards from Google, Microsoft and Facebook in addition to funding from DARPA and the NSF.