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Christopher H. Lin
Researcher at Microsoft
Publications - 27
Citations - 897
Christopher H. Lin is an academic researcher from Microsoft. The author has contributed to research in topics: Crowdsourcing & Markov decision process. The author has an hindex of 12, co-authored 26 publications receiving 740 citations. Previous affiliations of Christopher H. Lin include University of Washington.
Papers
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Journal ArticleDOI
POMDP-based control of workflows for crowdsourcing
TL;DR: In this paper, decision-theoretic techniques for the problem of optimizing workflows used in crowdsourcing are presented, where AI agents that use Bayesian network learning and inference in combination with Partially-Observable Markov Decision Processes (POMDPs) for obtaining excellent cost-quality tradeoffs.
Journal ArticleDOI
Task-Oriented Dialogue as Dataflow Synthesis
Jacob Andreas,John Bufe,David Burkett,Charles Chen,Josh Clausman,Jean Crawford,Kate Crim,Jordan DeLoach,Leah Dorner,Jason Eisner,Hao Fang,Alan Guo,David Hall,Kristin Hayes,Kellie Hill,Diana Ho,Wendy Iwaszuk,Smriti Jha,Dan Klein,Jayant Krishnamurthy,Theo Lanman,Percy Liang,Christopher H. Lin,Ilya Lintsbakh,Andy McGovern,Aleksandr Nisnevich,Adam David Pauls,Dmitrij Petters,Brent Read,Dan Roth,Subhro Roy,Jesse Daniel Eskes Rusak,Beth Short,Div Slomin,Ben Snyder,Stephon Striplin,Yu Su,Zachary Tellman,Sam Thomson,Andrei Vorobev,Izabela Witoszko,Jason Wolfe,Abby Wray,Yuchen Zhang,Alexander J. Zotov +44 more
TL;DR: An approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph, which enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict.
Proceedings Article
Personalized Online Education — A Crowdsourcing Challenge
Daniel S. Weld,Eytan Adar,Lydia B. Chilton,Raphael Hoffmann,Eric Horvitz,Mitchell Koch,James A. Landay,Christopher H. Lin +7 more
TL;DR: Some of the challenges and directions that HCOMP researchers will address are sketched, and some of the directions researchers hope to address are outlined.
Proceedings ArticleDOI
Effective Crowd Annotation for Relation Extraction
TL;DR: This paper demonstrates that a much larger boost is possible in crowdsourced annotation of training data boost performance for relation extraction over methods based solely on distant supervision, thanks to a simple, generalizable technique, Gated Instruction.
Proceedings Article
Dynamically switching between synergistic workflows for crowdsourcing
TL;DR: It is shown that alternative workflows can compose synergistically to yield much higher quality output and design and implement AGENTHUNT, a POMDP-based controller that dynamically switches between these workflows to achieve higher returns on investment.