Christopher Clark

I am a fourth year graduate student at the University of Washington, where I am advised by Luke Zettlemoyer. I am interested in question answering, and more generally in training models to reason about text. My recent work has been motivated by considering the limitations of what models learn from current question answering datasets.

Before coming to the University of Washington I was a Predoctoral Young Investigator at the Allen Institute for Artificial Intelligence, and before that I got a Masters at the University of Edinburgh.


Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Christopher Clark, Mark Yatskar, Luke Zettlemoyer
In EMNLP 2019
[paper] [code]

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, Kristina Toutanova
In NAACL 2019
[paper] [dataset] [leaderboard]

Simple and Effective Multi-Paragraph Reading Comprehension
Christopher Clark, Matt Gradner
In ACL 2018
[paper] [code] [demo]

Deep Contextualized Word Representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
In NAACL 2018
[paper] [website]

IKE - An Interactive Tool for Knowledge Extraction
Bhavana Dalvi, Sumithra Bhakthavatsalam, Chris Clark, Peter Clark, Oren Etzioni, Anthony Fader, Dirk Groeneveld
In AKBC at NAACL 2016
[paper] [website] [code]

PDFFigures 2.0: Mining Figures from Research Papers
Christopher Clark, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, Vicente Ordonez.
In JCDL 2016
[paper] [website] [code]

Looking Beyond Text: Extracting Figures, Tables, and Captions from Computer Science Papers
Christopher Clark, Santosh Divvala
In Workshop on Scholarly Big Data at AAAI 2015
[paper] [website] [code]

Training Deep Convolutional Neural Networks to Play Go
Christopher Clark, Amos Storkey
In ICML 2015
[paper] [demo]


CSE 424
Paul G. Allen Center for Computer Science & Engineering
Email: csquared [at] cs [dot] washington [dot] edu