no code implementations • COLING 2020 • Yuning Ding, Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch
Automatic content scoring systems are widely used on short answer tasks to save human effort.
no code implementations • WS 2020 • Brian Riordan, Sarah Bichler, Allison Bradford, Jennifer King Chen, Korah Wiley, Libby Gerard, Marcia C. Linn
With the widespread adoption of the Next Generation Science Standards (NGSS), science teachers and online learning environments face the challenge of evaluating students{'} integration of different dimensions of science learning.
no code implementations • WS 2020 • Aoife Cahill, James H Fife, Brian Riordan, Avijit Vajpayee, Dmytro Galochkin
The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately.
no code implementations • WS 2020 • Anastassia Loukina, Nitin Madnani, Aoife Cahill, Lili Yao, Matthew S. Johnson, Brian Riordan, Daniel F. McCaffrey
The effect of noisy labels on the performance of NLP systems has been studied extensively for system training.
no code implementations • WS 2019 • Brian Riordan, Michael Flor, Robert Pugh
Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in in word token-based input.
no code implementations • NAACL 2018 • Su-Youn Yoon, Aoife Cahill, Anastassia Loukina, Klaus Zechner, Brian Riordan, Nitin Madnani
In large-scale educational assessments, the use of automated scoring has recently become quite common.
no code implementations • WS 2018 • Michael Flor, Brian Riordan
We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis.
no code implementations • WS 2017 • Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch, Chong MIn Lee
Neural approaches to automated essay scoring have recently shown state-of-the-art performance.
no code implementations • COLING 2016 • Swapna Somasundaran, Brian Riordan, Binod Gyawali, Su-Youn Yoon
This work investigates whether the development of ideas in writing can be captured by graph properties derived from the text.