Events

Search Mastery Speaker Series: Manchester vs. Cranfield

Event Start Date: Thursday, March 17, 2022 - 10:00 am

Event End Date: Thursday, March 17, 2022 - 11:00 am

Location: Virtual

Add to Calendar Thursday, March 17, 2022 10:00 am Thursday, March 17, 2022 11:00 am America/New York Search Mastery Speaker Series: Manchester vs. Cranfield

Manchester vs. Cranfield: Why do we have computers answering
questions from web search data and how can we do it better?

Dr. Boyd-Garber

Abstract:
In this talk, I’ll argue that the intellectual nexus of computers searching through the web to answer questions comes from research undertaken in two mid-century English university towns: Manchester and Cranfield.

After reviewing the seminal work of Cyril Cleverdon and Alan Turing and explaining how that shaped today the information and AI age, I’ll argue that these represent two competing visions for how computers should answer questions: either exploration of intelligence (Manchester) or serving the user (Cranfield).

However, regardless of which paradigm you adhere to, I argue that the ideals for those visions are not fulfilled in modern question answering implementations: the human (Ken Jennings) vs. computer (Watson) competition on Jeopardy! was rigged, other evaluations don’t show which system knows more about a topic, the training and evaluation data don’t reflect the background of users, and the annotation scheme for training data is incomplete.

After outlining our short-term solutions to these issues, I’ll then discuss a longer-term plan to achieve the goals of both the Manchester and Cranfield paradigms.

Bio:
Jordan Boyd-Graber is an associate professor in the University of Maryland’s Computer Science Department, iSchool, UMIACS, and Language Science Center. Jordan’s research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process. He and his students have won “best of” awards at NIPS (2009, 2015), NAACL (2016), and CoNLL (2015), and Jordan won the British Computing Society’s 2015 Karen Spärk Jones Award and a 2017 NSF CAREER award.


The Search Mastery Special Interest Group at the iSchool is pleased to invite you to a Webinar series focused on Search Mastery and Information Literacy. Over the Spring semester 2022 we will host a number of speakers to share their insights on the challenges and opportunities for advances in this critical area.

The talks are virtual. The speaker series will highlight current thought about search skills and search education including concepts, theories, and helpful models. We hope to understand the basic questions, fundamental puzzles, and empirical unknowns that if answered could transform how we approach search mastery.

Virtual

Manchester vs. Cranfield: Why do we have computers answering
questions from web search data and how can we do it better?

Dr. Boyd-Garber

Abstract:
In this talk, I’ll argue that the intellectual nexus of computers searching through the web to answer questions comes from research undertaken in two mid-century English university towns: Manchester and Cranfield.

After reviewing the seminal work of Cyril Cleverdon and Alan Turing and explaining how that shaped today the information and AI age, I’ll argue that these represent two competing visions for how computers should answer questions: either exploration of intelligence (Manchester) or serving the user (Cranfield).

However, regardless of which paradigm you adhere to, I argue that the ideals for those visions are not fulfilled in modern question answering implementations: the human (Ken Jennings) vs. computer (Watson) competition on Jeopardy! was rigged, other evaluations don’t show which system knows more about a topic, the training and evaluation data don’t reflect the background of users, and the annotation scheme for training data is incomplete.

After outlining our short-term solutions to these issues, I’ll then discuss a longer-term plan to achieve the goals of both the Manchester and Cranfield paradigms.

Bio:
Jordan Boyd-Graber is an associate professor in the University of Maryland’s Computer Science Department, iSchool, UMIACS, and Language Science Center. Jordan’s research focus is in applying machine learning and Bayesian probabilistic models to problems that help us better understand social interaction or the human cognitive process. He and his students have won “best of” awards at NIPS (2009, 2015), NAACL (2016), and CoNLL (2015), and Jordan won the British Computing Society’s 2015 Karen Spärk Jones Award and a 2017 NSF CAREER award.


The Search Mastery Special Interest Group at the iSchool is pleased to invite you to a Webinar series focused on Search Mastery and Information Literacy. Over the Spring semester 2022 we will host a number of speakers to share their insights on the challenges and opportunities for advances in this critical area.

The talks are virtual. The speaker series will highlight current thought about search skills and search education including concepts, theories, and helpful models. We hope to understand the basic questions, fundamental puzzles, and empirical unknowns that if answered could transform how we approach search mastery.

Register for this event